Integrating nanotechnology with deep learning for engineering, healthcare, and environmental solutions: A review

¹ Department of Electrical Engineering, School of Engineering, RK University, Rajkot, 360020, Gujarat, India
² Department of Microbiology, School of Science, RK University, Rajkot, 360020, Gujarat, India
³ Department of Microbiology, Krishna School of Science, Drs. Kiran and Pallavi Patel Global University, Vadodara, Gujarat, India
⁴ University Centre for Research and Development (UCRD), Chandigarh University, Gharuan, Mohali, 140413, Punjab, India

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Published online: 21 January 2026

Abstract

Combining nanotechnology and deep learning methods is a leading-edge frontier in engineering solutions, with unparalleled prospects for innovation and growth. The synergistic combination blends the scale and precision of nano-materials with the predictive capability and resilience of artificial intelligence. Deep learning methods can process enormous amounts of data collected by nano-scale sensors, designing and optimizing materials at the atomic level. This integration makes possible the creation of self-healing smart nano-materials, highly efficient energy storage devices, and ultra-sensitive healthcare diagnostic tools. Additionally, deep learning methods can expedite the discovery of new nanostructures and their uses with much less time and expense compared to conventional experimental approaches. As this multidisciplinary area continues to grow, it holds the potential to transform numerous industries, such as electronics, healthcare, environmental science, and energy technology, towards more efficient, sustainable, and smart engineering solutions.