In this study, we investigate Bitcoin price volatility from December 15, 2014 to January 29, 2024 using an integrated, multisource feature set and an optimization–learning pipeline that couples Taguchi orthogonal arrays with a backpropagation network (BPN) implemented in MATLAB. Publicly available market variables were prioritized and nonquantifiable exogenous shocks were not modeled; Taguchi screening identified critical predictors and simultaneously tuned control factors (network specification, hidden-neuron count, and currency inclusion), after which the BPN was trained on aligned weekly (n = 573) and monthly (n = 108) datasets to ensure cross-market comparability. Model accuracy, assessed by mean absolute percentage error (MAPE), improved substantially after Taguchi-guided selection and configuration—weekly MAPE decreased from 3.23% to 0.36% and monthly MAPE from 6.32% to 0.07%—demonstrating the efficacy of the proposed optimization framework. Out-of-sample forecasts for February–April 2025 achieved predominantly sub-10% MAPE, while high-error instances were analyzed and attributed to contributing factors, yielding decision-relevant insights for practitioners and researchers. Collectively, the results show that systematic variable selection and orthogonal-array–based model design materially enhance neural forecasts of cryptocurrency prices and provide a reproducible pathway to accurate, time-efficient prediction.