Managerial pricing decisions are central to organizational profitability but are often compromised by systematic cognitive biases. This study investigates how anchoring, loss aversion, and overconfidence distort managerial pricing judgments and identifies the psychological mechanisms through which these effects occur. The study pursues four objectives: first, to quantify the individual effects of anchoring, loss aversion, and overconfidence on pricing accuracy; second, to examine their joint and interactive influence on pricing distortions; third, to develop and empirically test a bias-corrected managerial pricing framework integrating behavioral factors; and fourth, to generate robust empirical evidence that advances the fragmented behavioral pricing literature and informs debiased pricing practices. Using a between-subjects experimental design, 240 experienced managers were randomly assigned to anchoring, loss-aversion, overconfidence, or control conditions and completed a realistic pricing simulation. Pricing error was measured as deviation from optimal benchmarks, alongside assessments of cognitive distortion and confidence bias. Results show that all three biases significantly increased pricing errors, with anchoring and loss aversion exerting the strongest direct effects. Mediation and structural equation modeling reveal that cognitive distortion is the primary pathway through which bias-inducing conditions translate into pricing errors, while confidence bias plays a secondary but reinforcing role, particularly under overconfidence. When multiple biases co-occur, their effects compound, producing larger deviations from optimal prices. The findings make a theoretical contribution by providing an integrated, pricing-specific account of multiple managerial biases and empirically validating a dual-mediation framework linking bias, cognition, and pricing outcomes. Practically, the results highlight the value of structured decision protocols, calibration training, and decision-support systems as effective interventions for improving pricing accuracy and managerial decision quality.