In the rapidly evolving landscape of e-commerce, pricing strategies play a pivotal role in driving customer engagement, maximizing revenue, and maintaining competitiveness. Traditional pricing models often fail to account for real-time market fluctuations and individual customer behavior. This study explores the integration of machine learning (ML) techniques to develop dynamic pricing strategies in e-commerce platforms. By leveraging large-scale data such as customer preferences, purchase history, competitor pricing, inventory levels, and seasonal trends, ML algorithms can identify optimal price points that adjust in real time. The research highlights key machine learning models, including regression analysis, reinforcement learning, and deep learning networks that enable predictive and adaptive pricing mechanisms. It also examines case studies from leading e-commerce firms to demonstrate practical implementations and outcomes. The findings indicate that ML-based dynamic pricing not only enhances profitability but also improves customer satisfaction through personalized and timely pricing. This paper concludes by addressing challenges such as ethical concerns, price discrimination risks, and algorithmic transparency, while proposing guidelines for responsible adoption in digital marketplaces.