This study investigates the role of artificial intelligence (AI) in optimizing digital business processes within platform-based commercial environments. The research proposes an integrated AI-driven framework that combines predictive, prescriptive, and adaptive models to enhance operational efficiency, process throughput, and economic performance. Empirical analysis was conducted using transactional and operational data from a mid-scale e-commerce platform, comprising over two million transactions. Hybrid machine learning models, including LSTM neural networks and gradient boosting, were employed to forecast demand, customer behavior, and process outcomes, achieving up to 92% predictive accuracy. Reinforcement learning algorithms and prescriptive analytics optimized inventory management, workflow scheduling, and dynamic pricing. Implementation of the AI-driven framework resulted in significant improvements: 18% reduction in order processing time, 22% increase in inventory turnover, 12% higher revenue per transaction, and 14% reduction in operating costs. Comparative analysis demonstrated that AI-based optimization significantly outperforms traditional rule-based methods across operational and economic dimensions. In addition, the study demonstrates that AI-enabled platforms foster higher levels of customer personalization and service responsiveness, leading to improved customer satisfaction and retention rates. The modular architecture of the framework allows seamless integration with existing enterprise resource planning (ERP) and customer relationship management (CRM) systems, facilitating practical implementation in diverse digital commerce ecosystems.