India has emerged as one of the world's most dynamic and rapidly advancing centers for scientific research powered by machine learning (ML), according to a newly released report. ML Global Impact Report 2025 Written by Mark Tech Post. The new dataset shows that India is rapidly strengthening its position in global AI-driven science, ranking third globally in ML-powered research published across the Nature family of journals.
This study nature A family of journals study from January 1 to September 30, 2025 identifies India as the third largest contributor to ML-supported scientific output worldwide, after China and the United States.
India's rise reflects a growing network of universities, healthcare institutions, national laboratories, deep tech startups, and AI research centers that are applying ML to address the country's most complex scientific and societal challenges. ML has become a fundamental tool in India's scientific ecosystem, driving innovation across areas critical to the nation's development.
Rapid growth of ML-driven scientific research in India
Indian researchers have demonstrated widespread adoption and application of widely used ML frameworks (XGBoost, Transformers, ResNet, U-Net, YOLO, LightGBM, CatBoost, etc.) to high-impact scientific fields, including:
- Medical imaging, diagnosis, cancer screening, genomics
- Climate science, monsoon forecasting, environmental modeling
- Agriculture, crop yield forecasting, and food system resilience
- Materials science, chemistry, nanotechnology
- Earth observation, remote sensing, disaster prevention
This wide range of applications highlights India's strong alignment with national priorities in health, agriculture, climate resilience, and sustainable development, and its focus on machine learning research that is practical, scalable, and socially relevant.
Research volume and density: India's expanding scientific footprint
The report shows that while China leads in research volume and the US in specialty breadth, India is experiencing a rapid trajectory in machine learning-driven science, with more institutions joining each year.
India's expansion is supported by:
- A growing interdisciplinary research cluster
- Increased investment in AI for health, agriculture, and climate change
- Significant contributions from both first and second tier universities
- A fast-growing startup ecosystem that transforms research into applied innovation
India's participation is becoming increasingly decentralized and collaborative, positioning India as one of the fastest growing ML-enabled scientific ecosystems in the world.
Collaboration: India's strength in scientific partnerships
Similar to global ML research, Indian scientific output is highly collaborative, with most ML-based research involving 2 to 15 affiliated institutions. Collaborations in India often involve:
- Academic and medical institutions
- Computational Laboratory and Faculty of Engineering
- Public research institutions and industry partners
- Deep tech startups and clinical institutions
International cooperation is of particular importance and is evident in India's partnerships with the following countries:
- USespecially in health, genomics, and climate.
- Saudi Arabiaespecially materials science and applied machine learning
- global research network Work in computer vision, environmental science, and agriculture
These collaboration patterns demonstrate India's increasing integration into the global ML research community.
Beyond generative AI: Classic ML strengthens India’s scientific impact
Despite the popularity of generative AI models, the report reveals that India's scientific progress is primarily driven by mature and proven machine learning techniques, reflecting global trends. Classical ML techniques such as Random Forests, SVM, and Scikit-based workflows account for 47% of all ML use cases worldwide, and these approaches remain at the center of research output in India.
When combined with established ensemble approaches such as GBM, XGBoost, LightGBM, and CatBoost, these traditional techniques represent over 75% of the ML techniques powering real-world scientific research. This reinforces India's focus on practical and scalable innovations rather than hype-based experiments.
The Indian research environment primarily uses ML for application-oriented scientific tasks such as prediction, early diagnosis, environmental modeling, and agricultural optimization, where classical and ensemble ML techniques have immediate real-world impact.
India in the global context: Top three scientific powers
India's third place ranking highlights the country's growing influence in global ML-driven science. This report places India within a broader ecosystem formed by fundamental ML tools derived from:
- US (core ML infrastructure)
- Canada (gun)
- England (alpha fold)
- Germany (Younet)
- France/EU (Scikit-Learn)
Russia (Cat Boost)
India's growing research footprint shows how the country is actively contributing to and benefiting from the global ML innovation landscape.
Industry commentary
Dr. Geetha Manjunath, Founder, CEO & CTO, NIRAMAI Health Analytix
“The explosion of machine learning-driven scientific research in India, particularly in medical imaging, diagnostics, and genomics, is shaping a future where advanced technologies will lead to large-scale population health improvements.”
NIRAMAI's Thermalytix® platform is a prime example of India's ability to transform ML-assisted scientific research into clinically validated, affordable, and globally scalable healthcare innovations. This technology enables early detection of breast cancer without the need for radiation, compression, or on-site radiologists and is particularly suitable for population-wide screening in low-resource settings.
“Solutions like Thermalytix® demonstrate that the Indian innovation ecosystem is using ML to develop unbiased medical technologies that have real impact on millions of people,” she added.
Asif Razak, Marktechpost Editor and Co-Founder
“The rise of ML-powered scientific research in India is one of the most notable trends in this dataset. What stands out is the country's ability to apply machine learning to diverse scientific domains, from agriculture and health to climate and engineering. India is solidifying its position as a major contributor to the global ML research ecosystem.”
methodology
This analysis examined all ML-related scientific papers from around the world. nature Portfolio from January 1 to September 30, 2025. An integrated Python-based pipeline identified articles with ML flags and extracted:
- scientific field
- Author and country affiliation
- ML tools used
- Scientific contributions enabled by ML
- Citation information (if available)
Tools frequently used in the Indian research ecosystem include Transformers, XGBoost, ResNet, U-Net, YOLO, LightGBM, CatBoost, and BERT, demonstrating widespread and mature integration of ML across scientific fields in India.
About Marktech Post
Marktechpost is a global publication covering artificial intelligence, machine learning, and emerging technology research. The platform highlights advances made by academic institutions, research institutions, and practitioners that are shaping the future of applied AI. https://www.marktechpost.com/
