Predictive maintenance has emerged as a strategic mainstay in the operation of most modern industries with the integration of advanced data analytics for enhancing both equipment reliability and operational efficiency. This paper examines how sensor data is integrated with machine learning in predicting and preventing equipment failures across industries. Time-series data is collected from various sensors installed in the industrial machinery, after which several machine learning models are developed and evaluated; these include regression, classification, and anomaly detection algorithms. The case study indicates how such models can be employed to predict equipment failures and recommend timely maintenance for reducing unplanned downtime and overall maintenance costs. Therefore, precision, recall, and F1-score are the three important metrics that have been used for different models in terms of performance; hence, effectiveness in real-world scenarios. These results hint that with machine learning- based predictive maintenance, huge improvements in operational efficiency and longevity could be achieved, therefore giving a solid backbone to future research and practical applications of industrial strategies regarding maintenance of Industrial Equipment.