Dynamic pricing, the practice of algorithmically adjusting prices in real time based on demand fluctuations, inventory levels, competitor pricing, and individual consumer characteristics, has become pervasive across e-commerce, travel booking, ride-hailing, and streaming platforms, yet its widespread adoption continues to generate consumer ambivalence rooted in perceptions of price fairness and vendor trustworthiness. This paper reviews the psychological and behavioral mechanisms linking dynamic pricing exposure to consumer fairness judgments, trust formation, and downstream purchase intentions, drawing on dual entitlement theory, distributive and procedural justice frameworks, and price-fairness perception research. The study examines the distinction between demand-based dynamic pricing, in which prices fluctuate uniformly for all consumers based on aggregate market conditions, and personalized dynamic pricing, in which prices are algorithmically tailored to individual consumers based on browsing history, purchase behavior, or inferred willingness to pay, situating each within documented consumer response evidence. The review further examines the moderating roles of price transparency, perceived firm motive, and reference-price availability in shaping fairness judgments, alongside empirical evidence on the reputational and behavioral consequences of consumer-perceived price discrimination. A comparative methodology is described using survey-based price-fairness perception scales, scenario-based experimental designs manipulating pricing transparency and personalization disclosure, and behavioral intention measures including purchase intention, brand switching, and negative word-of-mouth. Reported findings indicate that demand-based surge pricing is judged substantially less unfair than individually personalized pricing at an equivalent price level, that transparency regarding the pricing mechanism meaningfully attenuates perceived unfairness, and that perceived unfair pricing produces measurable declines in purchase intention and trust alongside increased negative word-of-mouth propensity. The paper concludes by discussing implications for e-commerce pricing strategy design, disclosure policy, and platform trust management, alongside persistent gaps in understanding long-term consumer adaptation to algorithmic pricing.