This paper introduces CMI4.1, an enhanced and data-driven smart maintenance maturity framework designed to advance the implementation of Condition-Based Maintenance (CBM) and improve the estimation of Remaining Useful Life (RUL) of mechanical components, specifically within the context of Pakistan’s aircraft manufacturing sector. The proposed model builds upon the previously published CMI4.0, which was developed by the same authors as a localized adaptation of the IMPULS Industry 4.0 readiness model by the German Mechanical Engineering Association (VDMA) and refined through expert consensus using the Delphi method. CMI4.1 expands the scope of its predecessor by incorporating additional enablers aligned with intelligent manufacturing environments, with a strong focus on digitalization, data integration, and AI readiness. The framework leverages machine learning and deep learning algorithms to analyze high-dimensional sensor data, enabling predictive maintenance, early anomaly detection, and real-time decision-making. Empirical validation within industrial settings confirms that CMI4.1 enhances CBM readiness assessments, minimizes unplanned downtime, improves operational reliability, and offers a scalable roadmap for smart maintenance transformation in emerging economies.