The rise of digital platforms has profoundly transformed modern markets, particularly through the deployment of algorithmic pricing strategies powered by big data. As firms increasingly rely on sophisticated algorithms to set prices dynamically, questions arise about the implications for market efficiency and consumer welfare. This paper explores how algorithmic pricing, when implemented on data-rich digital platforms, affects competitive behavior, price transparency, and consumer outcomes. While algorithmic systems can theoretically enhance efficiency by matching prices more closely to real-time demand and supply conditions, they may also facilitate tacit collusion, reduce price dispersion, and undermine traditional competitive dynamics. The power of big data enables platforms to segment consumers, personalize prices, and predict purchasing behavior with unprecedented accuracy, raising concerns about fairness, privacy, and market manipulation. Additionally, the opacity of algorithmic processes poses regulatory challenges in ensuring that pricing strategies align with pro-competitive principles and consumer protection goals. This study contributes to the growing discourse on the economic consequences of digitalization by examining how algorithmic pricing impacts allocative efficiency, price stability, and surplus distribution. Ultimately, the paper underscores the dual potential of these technologies to foster innovation and efficiency while also risking distortions that may harm consumer welfare and weaken competition in increasingly data-driven markets.