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Digital pricing has become a central managerial capability in online business models, where firms increasingly move beyond fixed prices toward dynamic, algorithmic, and personalised pricing systems. This shift reflects the growing availability of behavioural data, competitive intelligence, platform analytics, and machine learning tools that allow prices to be adjusted more frequently and precisely. However, the same capabilities that support revenue optimization also intensify concerns about fairness, transparency, and customer trust. This systematic review examines how digital pricing management has been studied across pricing strategy, revenue management, consumer psychology, retailing, tourism, platform economics, and algorithmic governance. The review focuses on three connected themes: dynamic pricing and revenue optimization, customer fairness and trust perceptions, and managerial control of automated pricing practices. The aim is to clarify what is known, where evidence remains fragmented, and how future research can integrate performance and governance perspectives. Following a PRISMA-informed approach, the review synthesises peer-reviewed journal articles published between. The selected evidence includes conceptual, empirical, modelling, experimental, and review-based contributions relevant to digital pricing in online and data-rich business environments. The review does not introduce new empirical data but systematically collates and interprets existing research. The review concludes that digital pricing management should be understood as both a revenue optimization system and a trust-sensitive managerial practice. Firms need pricing architectures that combine analytical precision with transparency, accountability, and human oversight. Future research should develop integrated models that examine profitability, fairness, governance, and long-term customer relationships together rather than as separate research streams.