<p>Highwall mining presents a cost-effective and safe method for extracting coal from reserves that are otherwise inaccessible due to high stripping ratios or geographical constraints in open-pit mines. However, the performance and productivity of highwall mining machines (HWMs) are critically dependent on their reliability, maintainability, and availability (RMA). This study addresses a significant research gap by conducting a comprehensive RMA analysis of HWM systems over a 15-year operational period using real-world failure and repair data from eight major subsystems. The study employs a Markov Chain modeling framework to quantify time-dependent reliability degradation and repair behavior, particularly highlighting the Cutter Head Group (CHG) and Hydraulic Group (HG) as the Most failure-prone components. Despite notable reliability deterioration from 0.8 to 0.62 in CHG, effective maintenance practices have sustained an overall system availability of 74%. The novelty of this research lies in the integration of a Markov-based reliability modeling approach with a reliability-centered maintenance (RCM) framework specifically tailored for highwall mining operations. Unlike prior studies that focus mainly on design or stability aspects, this work introduces a subsystem-level probabilistic analysis, offering predictive insights into long-term performance trends. Additionally, the study provides statistically validated models to support predictive maintenance planning, optimize component replacement schedules, and enhance lifecycle cost efficiency. The findings contribute not only to improving the operational sustainability of HWM systems but also set a precedent for applying analytical reliability tools in large-scale mining equipment. This dual-layered modeling approach offers a novel pathway for continuous performance enhancement, ultimately ensuring uninterrupted coal production and safer mining environments.</p>