Artificial intelligence (AI) has become a game-changing technology in several areas, including choice of strategy and decision-making. This study examines how the EDAS (Evaluation based on Distance from Average Solution) is used to improve the process of selecting a strategy for difficult problem-solving scenarios using AI approaches. The EDAS method enables decision-makers to rank options according to how close they are to the average solution. It is a successful multi-criteria decision-making strategy. The research’s Significance is that Run-to-failure maintenance, preventative maintenance, condition-based maintenance, and reliability-centered maintenance are all abbreviations for the same thing. Using the EDAS Method, the evaluation metrics for this maintenance strategy are reliability, cost, safety, availability, and downtime. Use the EDAS method, a multi-criteria decision-making methodology, to rank the options (strategies) following how far apart they are from the typical solution. Utilising the normalized data and criteria weights, determine the EDAS scores for each method. The study resulted in the maintenance strategies, condition-based maintenance (CBM) ranked first, and run-to-failure maintenance (RTFM) ranked last, showing the parameters as Reliability, Cost, Safety, Availability, and Downtime.