Brake pad wear is a major and growing source of non-exhaust particulate emissions, projected to reach 1.3 million tons annually by 2030 and contributing up to roughly 55% by mass of non-exhaust traffic-related PM10 in urban environments, underscoring the need for improved durability and material optimization. This study investigates a three-stage eXtreme Gradient Boosting (XGBoost) ensemble paired with a residual Fully Connected Neural Network (FCNN) corrector to predict brake pad wear rate and support formulation optimization. Experiments used a simplified FMVSS 135 protocol on a Universal Mechanical Tester (UMT) simulating realistic braking across eight friction regimes. Wear rate was the sole machine-learning prediction target, while coefficient of friction (CoF) was retained as an input feature rather than a target. Despite a limited but high-quality 280-cycle dataset, regime-aware stratified splitting, sample reweighting, and hyperparameter optimization enabled robust generalization. The three-stage XGBoost ensemble with residual FCNN correction achieved a global held-out test R2 of 0.976 for wear rate prediction. A Taguchi L8 design of experiments defined the brake pad compositions, reducing experimental time and material consumption compared to conventional approaches. The framework demonstrated strong agreement between measurements and predictions for the dominant low-severity regime, while per-regime analysis identified the high-severity minority regimes as the priority for additional data collection, since within-regime R2 remains negative for every regime given current sample sizes. A sequence-aware mean absolute scaled error (MASE) analysis further shows that, despite the high global R2, none of the four pipeline stages currently outperforms a naive one-cycle persistence forecast on absolute error, a distinction reported here for transparency. The scalable architecture enables straightforward integration of additional material and process parameters, supporting iterative brake formulation development in industrial settings and, by reducing empirical testing requirements, sustainable brake material development with reduced replacement frequency and associated emissions.