In an increasingly digitized and hypercompetitive market landscape, pricing is evolving from a static, cost-based process into a dynamic, strategic function powered by Artificial Intelligence (AI). Organizations are moving beyond traditional pricing models by leveraging real-time analytics, predictive modelling, and behavioural insights to enhance pricing decisions. AI enables large-scale automation and personalization of prices, allowing firms to adapt continuously to changes in demand, consumer behavior, and competitive signals. However, this shift also raises critical concerns regarding transparency, fairness, and algorithmic accountability—especially in consumer-facing markets, where price sensitivity and perceptions of justice directly influence trust and loyalty. This study shows how AI reshapes pricing across four impact areas—predictive modelling, real-time optimisation, behavioural adaptation, explainability and fairness—and, through a systematic literature review, distils a two-level taxonomy of AI applications in pricing. The findings demonstrate that AI-powered pricing enhances forecasting accuracy, enables granular and dynamic price optimization, and improves behavioural targeting through the integration of cognitive heuristics such as anchoring and loss aversion. These benefits, however, must be balanced against the heightened risks of opacity, unfair discrimination, and regulatory non-compliance, which can erode consumer trust. The study emphasises that AI is not merely a technological tool, but a transformative agent that reshapes market dynamics and redefines consumer expectations. As such, the adoption of transparent, explainable, and ethically aligned AI models is essential—not only to ensure compliance with emerging regulatory frameworks, but also to foster long-term consumer confidence and sustainable competitive advantage. The paper concludes by calling for interdisciplinary research to explore the longitudinal effects of AI-based pricing, ethical governance mechanisms, and the integration of social responsibility into algorithmic pricing strategies.