The growing complexity of modern industrial systems demands advanced fault diagnosis and predictive maintenance strategies to ensure reliability, safety, and cost-effectiveness. This paper investigates the use of data mining, machine learning, and big data analytics for fault detection and predictive maintenance. Smart sensor networks enable real-time diagnostics and early fault prediction. The application of big data mining techniques helps uncover meaningful patterns, improving decision-making. Genetic algorithms optimize predictive models, enhancing fault classification and anomaly detection. The proposed methodology employs both supervised and unsupervised learning approaches to analyze sensor data, detect anomalies, and predict failures, thus reducing downtime and maintenance costs.