Abstract Financial markets exhibit changing volatility and regime-dependent behavior, making the interaction between deterministic trading rules and machine-learning validation an important empirical question. This study evaluates a Bollinger Band Mean Reversion (BBMR) strategy and examines whether a generic machine-learning filtering layer improves the characteristics of its candidate signals. The supplied experimental registry contains 30 independent configurations: five unfiltered baseline configurations and 25 ML-assisted configurations formed by crossing five timeframes—1-minute, 3-minute, 5-minute, 15-minute and 1-hour—with five confidence settings of 0.50, 0.53, 0.55, 0.57 and 0.60. Across the registry, 99,807 recorded orders are reported. The five baseline configurations show a descriptive aggregate win percentage of 66.55% and a row-wise Net Realized sum of 316,612.56. The 25 ML-assisted configurations show a descriptive aggregate win percentage of 19.85% and a row-wise Net Realized sum of -135,285.70. Increasing the ML confidence setting reduces recorded activity from 15,278 orders at 0.50 to 2,373 at 0.60, but the reduction in activity is not accompanied by a corresponding improvement in win percentage or aggregate descriptive profitability. The ML-assisted win percentage remains below the baseline at every tested timeframe, and only the 1-hour ML group has a positive row-wise Net Realized sum. An implementation-level inspection further indicates that the validation feature layer is dominated by generic contextual variables such as moving-average slope, momentum, volatility and risk/reward measures rather than direct Bollinger mean-reversion descriptors. The findings therefore suggest a strategy-dependent limitation: the tested ML filter changes the trade population substantially, but does not improve the evaluated BBMR strategy and may be mismatched to the short-horizon mean-reversion mechanism. The result should not be generalized to all ML methods or all BBMR implementations; it is an empirical finding for the supplied configuration. Keywords: Bollinger Bands, Mean Reversion, Machine Learning, Signal Validation, Algorithmic Trading, Feature Engineering, Multi-Timeframe Analysis, Backtesting, Strategy-Dependent Validation.