This book is a concise and integrative synthesis of the rapidly expanding literature on dynamic pricing in e-commerce. Algorithmic dynamic pricing has become a central strategic tool for brand manufacturers and retailers operating in e-commerce and platform-mediated markets. Prices are no longer static decisions but are continuously updated by algorithms that learn from demand, inventory, competition, and consumer behavior-while operating under governance, fairness, and operational constraints. Based on a systematic literature review of 101 high-quality journal articles and supported by bibliometric mapping, the book organizes existing research into four coherent streams: operational and algorithmic pricing, consumer perceptions and fairness, platform governance and ecosystems, and revenue management in services and hospitality. Beyond summarizing prior work, the book proposes a contemporary definition of dynamic pricing as an algorithmic, learning-based policy and outlines 17 research propositions that structure future inquiry. These propositions connect methodological challenges-such as learning under competition and non-stationarity-with actionable managerial levers for pricing design. Designed for researchers, (pre)doctoral students, and practitioners, the book provides a clear entry point into a fragmented field and offers practical insights for firms navigating pricing decisions in data-driven, policy-constrained online markets.