<p>The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has transformed machining processes, significantly boosting efficiency, accuracy, and sustainability. This systematic review analyzes 182 research articles, categorized into eight thematic clusters using VOSviewer software, based on author keywords from the Scopus database, following the PRISMA framework. These clusters comprise ‘advanced sensing and prognostics,’ ‘machine learning and optimization in manufacturing,’ sustainability group (‘energy efficiency and optimization techniques’, ‘smart and sustainable manufacturing’, ‘neural networks and energy management’), ‘intelligent machining processes,’ ‘advanced algorithms in machining,’ ‘lubrication and tool wear management,’ ‘CNC and deep learning applications,’ and ‘digital twins. A critical literature review of each cluster was conducted to identify key trends, challenges, and developments in AI, ML, and DL applied in machining operations. The vital results are presented in table format. The review reveals that AI-driven machining has significantly enhanced predictive maintenance, real-time process monitoring, and energy optimization, resulting in a reduction of machining energy consumption by up to 20%. ML and DL models have improved machining accuracy, tool wear prediction, and adaptive process control. While progress has been made, difficulties persist in merging AI models with industrial systems. This review also highlights significant research gaps in data quality, system adaptability, and the scalability of AI solutions when integrating AI and ML with practical machining applications. The review addresses these gaps by proposing techniques that improve model accuracy and reliability across various machining contexts and provides a roadmap for future advancements in intelligent manufacturing systems.</p>