The integration of artificial intelligence (AI) with nanotechnology is generating remarkable improvements by tackling fundamental difficulties in material design, defect detection, process optimization, and scientific information extraction. This research covers four important AI methodologies—Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and Natural Language Processing (NLP)—emphasizing their strengths, applications, and limitations in nanotechnology. ML excels in predictive modeling and classification, DL boosts imaging and flaw detection, RL optimizes autonomous nanorobotics, and NLP promotes effective information extraction from huge scientific data. Notwithstanding these capabilities, challenges such data reliance, computational complexity, and scalability remain, underscoring the necessity for domain-specific solutions and interdisciplinary collaboration. This paper identifies critical research gaps, offering practical insights for the advancement of AI-driven nanotechnology. The findings underline AI's transformative potential in changing material science, biomedical applications, and nanoscale engineering, paving the path for creative solutions at the molecular and atomic levels.