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The convergence of Artificial Intelligence (AI) and nanotechnology represents a groundbreaking paradigm shift in science and engineering, opening new frontiers in medicine, materials science, environmental sustainability, and beyond. This review explores the integration of AI with nanotechnology by first introducing their fundamental principles. Nanotechnology, concerned with manipulating matter at the atomic and molecular scale, is significantly empowered by AI's ability to process vast datasets, recognize patterns, and predict outcomes. The synergy between the two fields is analysed through various applications, including optimization of nanodevice design, accelerated material discovery, prototyping, smart biomaterials, and environmentally sustainable nanotechnological solutions. AI-driven models improve efficiency in nanofabrication and enhance decision-making in real-time applications such as nano-enabled diagnostics and therapeutic delivery systems. However, despite the promise, the integration faces several challenges, including the scarcity of high-quality nanoscale data, computational limitations, the complexity of molecular interactions, and a lack of standardization across research platforms. Additionally, the black-box nature of many AI models poses interpretability concerns, especially in sensitive applications such as nanomedicine. Regulatory, ethical, and infrastructural hurdles further complicate implementation, particularly in low-resource settings. The review highlights future prospects, including AI-augmented autonomous laboratories, quantum machine learning for nanoscale modelling, and intelligent nanorobotics for personalized healthcare. With proper ethical oversight and continued interdisciplinary collaboration, the fusion of AI and nanotechnology promises to revolutionize multiple industries and drive a new era of intelligent, scalable, and sustainable technological advancement.