This research focuses on improving the understanding and prediction of truck cabin inspection points that meet tolerance scores (PIST) using Coordinate Measuring Machines (CMM). The study integrates two main components: the development of an online digital platform for real-time aggregation and monitoring of PIST data from CMM inspections, and the creation of machine learning models for predicting PIST values. The platform provides continuous tracking of CMM data, enhancing the analysis and interpretation of inspection points. Machine learning models, built using Python-based regression techniques, are validated using performance metrics such as MAE, MSE, RMSE, and R2, to predict PIST values with high accuracy. This approach streamlines the inspection process, reducing reliance on manual methods, while providing detailed visual insights into patterns affecting tolerance adherence. The ultimate goal is to optimize truck cabin quality, enhance inspection efficiency, and establish a robust, data-driven approach to PIST prediction and monitoring, introducing more precision and automation into the inspection process.