Rapid prediction of cavity sound pressure spectra is essential for the acoustic design of high-speed vehicles. A spectral database is built from 20 flow conditions (4 Mach numbers × 5 angles of attack) and eight cavity measurement points, giving 160 condition–point combinations, each densely sampled at 1147 amplitude–frequency points over 6–7000 Hz. Four models—RF, XGBoost, LightGBM and SVM—are compared on two prediction tasks, flow-condition and spatial-position prediction, before and after Bayesian optimization. Optimization markedly improves in-distribution accuracy and reduces dominant-frequency amplitude errors under unseen conditions, but reverses the ranking: RF and LightGBM dominate at baseline, whereas XGBoost ranks first after tuning, attaining 0.76 dB in the flow-condition task and staying on a par with RF (0.09 dB apart) in the spatial-position task. Under unseen conditions the SVM degenerates to a constant output of 117.11 dB, essentially the training-target mean, owing to the locality of the RBF kernel—a structural limitation that tuning cannot remedy. The direction of the peak-amplitude bias is not uniform: systematic underestimation occurs in the spatial-position task, whereas after tuning XGBoost overestimates the peak at Ma = 1.5, the condition with the highest measured amplitude. No single model is optimal across all scenarios.