In the modern era, machine learning has brought about significant improvements in nearly every scientific field, including physics, materials science, chemistry, biology, computer science, and engineering. With the advent of cutting-edge nanotechnology, atoms are getting arranged at 1–100 nm scale, when combined with machine learning (ML) encompasses nano-devices, and nano-structures with unique characteristics and capabilities. The vast amount of data may be handled using ML algorithms with the aid of imaging and spectroscopic techniques. This enables faster and more useful analysis of nanoscale structures and characteristics. The current status of machine learning in nanotechnology and its applications at various fields are studied, and examined in this research. This paper also provides extensive study on principles, difficulties, and possibilities of using ML to study and development of nanotechnology, including concerns about data quality, interpretability, and ethics. It can also spur discoveries and innovations, by producing novel nanomaterials with enhanced electrical, optical, and mechanical properties as well as special functions for a variety of uses, viz. energy storage, sensing, and biomedicine.