High thermal loads on brake systems during extended descents followed by vehicle soak pose significant safety and durability risks. Excessive rotor or fluid temperatures can cause loss of braking efficacy, fluid degradation or evaporation, thermal fade, and accelerated component wear. This study uses time history data of brake disc and fluid temperatures collected during controlled hill descent events and subsequent soak (park) periods, and simultaneously logs environmental conditions (ambient temperature, humidity, wind speed and direction) and brake specifications (rotor, caliper geometry, pad material, fluid type, and vehicle mass). Seventy six test runs from a dedicated validation program are used, each providing time history records that form the basis of our analysis. From these records we extract phase specific samples (descent and soak phase) and engineer compact descriptors, such as start and peak temperatures, environmental factors, rolling statistics and contextual metadata to represent each event. We develop and evaluate machine learning regression and neural network models to predict the peak disc and brake fluid temperatures occurring during the descent and across the soak phase. Cross validation uses a leave one run / leave one profile out strategy to ensure generalization to unseen descent events. Models are evaluated with mean absolute error (MAE) and bias diagnostics, and feature importance analysis identifies dominant drivers of peak heating (initial and ambient temperature, road surface temperature, rotor and caliper geometry). The predictive models enable early warning of critical temperature spikes and support design and operational decisions (cooling design, allowable profiles and optimization). By delivering fast temperature estimates, they reduce reliance on computationally expensive CFD during early design, while CFD and experiments remain for final validation. We present workflow, model performance and uncertainty characterization.