The paper aims to improve Predictive Modelling PdM practices at Koradi Thermal Power Station by developing a customized machine-learning model for the unique conditions under which the power station operates. It will address a major need within thermal power plants to enhance maintenance strategies by predicting equipment failures and allowing optimal scheduling of maintenance works. The main aim is to verify how data quality and availability impact the effectiveness of PdM. Analyzing in detail how a well-integrated source of data, such as IoT sensors or operational logs, can enhance the predictive maintenance framework toward timely and effective interventions is the focus. The other crucial objective is the performance evaluation of various different algorithms on machine learning, such as regression analysis, decision trees, and random forests, with these assessments focusing on the more accurate ways to predict equipment failure. The comparison study will attempt to determine how different algorithms can help in achieving higher efficiencies in operations at Koradi by ultimately forming a strong maintenance strategy. Other important considerations in the analysis are to set some key performance indicators for optimization in maintenance. Thus, this research identifies and measures KPIs regarding cost savings, minimization of unplanned downtime, and overall equipment reliability for providing a comprehensive framework for evaluating the success of the predictive maintenance implementation. The results of this study are expected to contribute precious information to the field of maintenance optimization, helping act as a roadmap for other thermal power plants to adopt similar strategies. The objectives of the research are targeted in terms of providing avenues that facilitate a proactive maintenance culture where operations become more efficient, costs go down, and generally, the reliability of power generation systems will have increased.