<p>In today’s competitive manufacturing landscape, operational excellence hinges on maintaining seamless production workflows and minimizing unplanned downtime. The efficient operation of machining lines is essential in engine manufacturing plants to ensure high productivity and meet market demand. However, the machining line studied in this paper, consisting of 14 machines, has been experiencing frequent failures and maintenance issues. These issues lead to significant threats to revenue and productivity. Despite the importance of understanding and mitigating such challenges, existing research often fails to provide proactive, comprehensive, real-world analysis tailored to specific production environments. This gap highlights the need for a detailed Reliability, Availability, and Maintainability (RAM) analysis to identify critical issues, improve machine performance, and enhance overall plant efficiency. Leveraging detailed failure and maintenance time data collected over a year, this research applies data cleaning and statistical analysis techniques to derive key reliability and maintainability metrics. Unlike many existing studies that assume exponential distributions for failure and repair times, this work incorporates Semi-Markov Process (SMP) Modeling to capture non-exponential behaviour. This approach provides a more realistic assessment of machine availability. By computing RAM indices for each machine, this analysis identifies critical bottlenecks in the production line and offers actionable insights into their operational health. The study provides a comparative evaluation of machine failure frequencies and examines distinct RAM-related characteristics. Additionally, it includes reliability versus time plots for individual machines and the entire machining line, which help pinpoint areas for improvement. These findings serve as a foundation for optimizing maintenance practices, reducing downtime, and guiding the implementation of Reliability-Centred Maintenance (RCM) strategies.</p>