This study investigates the influence of five core cognitive biases—loss aversion, anchoring, social proof, framing effect, and impulse buying—on consumer decision-making in the digital marketplace. Employing a structured quantitative methodology, the research analyzed survey data from 300 participants using a multi-attribute behavioral economics model that integrates psychometric validation, logistic regression, and structural path analysis. The findings reveal that social proof and impulse buying are the most potent drivers, significantly increasing purchase probability while reducing decision time. Loss aversion and anchoring also demonstrated significant, though less pronounced, effects. The framing effect's influence was found to be more context-dependent. A key contribution of this research is the empirical validation of a model that quantifies the direct and mediated effects of these biases, with decision-making speed identified as a crucial mediator. The results offer actionable insights for marketers, consumer protection agencies, and policymakers by providing a ranked hierarchy of psychological triggers and underscoring the ethical need for transparency in digital commerce. The study highlights the necessity of integrating behavioral metrics into predictive models of consumer choice and sets a foundation for future research into algorithm-bias interactions and cross-cultural validation.