The foundation of contemporary technological progress, nanofabrication, requires accuracy and productivity in the construction of nanoscale devices and structures. A promising approach to improving nanofabrication processes and tackling issues like process variability, defect detection, and yield enhancement is the incorporation of machine learning algorithms. The use of machine learning in Nano process optimization is examined in this abstract, with particular attention paid to the fields of quality control, defect identification, and predictive modeling. From supervised to unsupervised learning, machine learning algorithms make it possible to find complex patterns in large datasets, which in turn makes it easier to optimize process parameters for increased productivity and yield. Additionally, sophisticated image recognition algorithms are used by machine learning-driven defect detection systems to recognize and categorize flaws in real-time, improving quality control procedures and cutting down on waste. This abstract clarifies the potential of machine learning to transform nanofabrication and open up new applications in electronics, medicine, and other fields through case studies and theoretical debates. To fully realize the promise of machine learning in nanoprocess optimization, however, further study and cooperation are required to address issues including data quality, model interpretability, and ethical issues.