Tool wear and fault detection are paramount to manufacturing operation effectiveness and dependability. The project suggests an artificial intelligence system for real-time tool wear prediction and fault prognostics using unsupervised learning and advanced data-driven methods. The system monitors data from sensors, including vibration, temperature, and acoustic emissions, embedded within the machinery. Clustering algorithms, anomaly detection algorithms, and dimensionality reduction algorithms (such as K-means, DBSCAN, and PCA) are applied to detect patterns and anomalies in tool behaviour in the absence of label data. Reinforcement learning (RL) algorithms are applied to optimize maintenance policies by learning machine interactions continuously. This autonomous mechanism facilitates early fault detection, minimizes surprise failures, and maximizes overall manufacturing productivity and cost-effectiveness.