Photometric redshifts are essential for large-scale structure analyses, yet extracting optimal point estimates and reliability measures from the probability distribution functions (PDZs) delivered by modern photo-z pipelines remains challenging. We introduce turboPDZ, a machine-learning framework using PDZ features and associated observables to optimize redshift point estimates and derive a data-driven reliability score. We apply it to the three independent HSC-SSP PDR3 pipelines (DEmP, DNNz, Mizuki) across Wide and Deep/UltraDeep layers. Each PDZ is compressed via PCA and combined with summary descriptors. A multilayer perceptron, optimized with Optuna under a composite objective (σ_(rm NMAD) and η_(0.15)), predicts z_(rm ml). A second network, trained in log-space with MAE and calibrated post hoc, yields an empirical error scale σ_(rm ml), from which the reliability score r_(rm ml) is derived via percentile ranking. Relative to catalog point estimates, z_(rm ml) improves both metrics in all six pipeline-layer combinations. Absolute median bias is smaller in four configurations and tied in two. The reliability score achieves the lowest retained-fraction AUC in 10 of 12 native pipeline-layer/metric comparisons. For the Mizuki template-fitting pipeline, catalog reliability indicators have AUC values up to about 13 times larger than those of r_(rm ml). Feature importance shows that point estimation is dominated by PCA and location statistics, whereas reliability estimation gives greatest weight to peak, PCA, and shape information. The framework is survey-agnostic, but each PDZ dataset requires its own fitted preprocessing, PCA basis, and trained models. The code will be released as a reusable Python library at github.com/valerio-marra/turboPDZ.