The difficulties and important factors to take into account when applying machine learning to the detection of hard disk drive failure are outlined in this study. It is critical to address the dynamic nature of storage workloads, sparse labeled data, and heterogeneous hardware configurations. There are constant hurdles in achieving real-time flexibility and finding a balance between processing efficiency and precision. Successful deployment requires constant model modifications to account for new failure patterns and reduce false positives and negatives. The abstract highlights how machine learning can help overcome these obstacles by improving hard drive failure early detection, reducing data loss, and maximizing system reliability. Hard drive failure is detected using machine learning methods. Decision tree, logistic regression and random forest are used and 67% accuracy is achieved by the random forest model which us optimal.