Tire pressure monitoring systems (TPMS) are essential for vehicle safety and performance as they help detect low tire pressure that impacts fuel efficiency, ride comfort, and overall safety. This study introduces a novel stacking ensemble model to improve the monitoring of nitrogen-filled pneumatic tires. Vibration signals, captured under four conditions such as idle, highspeed, normal, and puncture, using low-cost MEMS accelerometers, are processed to derive autoregressive moving average (ARMA), histogram, and statistical features. The J48 decision tree is employed for feature selection, enhancing classifier accuracy. Experiments with various machine learning classifiers show that the stacking ensemble approach significantly improves classification performance for ARMA (93.75%) and histogram (85.42%) features, thereby achieving higher accuracy than individual classifiers. These findings demonstrate that stacking ensembles can enhance TPMS capabilities, offering a cost-effective and accurate solution for real-time tire pressure monitoring. This advancement contributes to automotive safety and maintenance by enabling more reliable and precise TPMS.