<p>The transition to Industry 4.0 necessitates intelligent, data-driven solutions for complex manufacturing challenges, with the optimization of machining parameters representing a critical lever for enhancing productivity, quality, and sustainability. While a diverse array of artificial intelligence (AI) techniques including Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Fuzzy Logic Systems has been deployed for this purpose, the absence of a unified analytical framework hinders the systematic selection and application of these methods across varied machining contexts. This review addresses this gap by first establishing a novel taxonomy that classifies AI techniques along three dimensions: learning paradigm, optimization scope, and implementation mode. Applying this framework to a comprehensive analysis of the literature reveals that hybrid AI models, which synergistically combine multiple algorithms, consistently achieve superior performance, delivering 30–40% improvements in prediction accuracy and 20–35% gains in cost and energy efficiency compared to conventional and single-algorithm approaches. The effectiveness of any given method, however, is shown to be highly contingent on specific process characteristics, from turning and milling to additive manufacturing. Significant barriers to industrial adoption persist, primarily concerning data quality, model interpretability, and integration with existing infrastructure. Emerging trajectories, such as explainable AI (XAI), digital twin-enabled simulation, and deep reinforcement learning for adaptive control, are identified as essential for realizing real-time, resilient, and sustainable machining systems. By synthesizing current advancements within a structured taxonomy and critically evaluating future directions, this review provides both a scholarly synthesis and a practical decision-making framework for advancing intelligent machining optimization in the era of smart manufacturing.</p>