In the mining industry, efficiency is achieved as well as cost-effectiveness through reducing unplanned downtime. To achieve running that ensures efficiency, maximum support to running equipment has to be provided. It addresses the critical issue of unplanned downtime by offering a predictive approach toward maintenance using advanced ML techniques. The objective is to forecast the failure rate of mining excavators for a future period so that mine companies are best placed to proactively manage maintenance activities and optimize resource utilization. Economic Implications: A proper maintenance strategy would classify maintenance failure as a significant capital input. Any failure in such equipment would have very devastating consequences in terms of finances. Besides raising lost production opportunities, it invokes costly repair and replacement efforts. However, the more delayed the potential loss, the ripple effect brings about immediate financial loss that hits productivity and competitiveness in the mining sector. By safeguarding against unplanned downtime and optimizing maintenance practices, they can maximize the productivity and profitability of their operations.