The dynamic nature of consumer behavior in digital ecosystems necessitates the adoption of intelligent systems for marketing optimization. Machine Learning (ML) has emerged as a pivotal technology for transforming digital marketing by enabling real-time decision-making based on multimodal consumer interaction data. At a macro level, ML empowers marketers to transcend rule-based targeting by continuously learning from diverse user data streams including click behavior, social media activity, device usage, sentiment in reviews, and past purchases. This rich interaction data is fused to model customer intent, optimize campaign delivery, and forecast conversions with high precision. Techniques such as gradient boosting, recurrent neural networks, and deep learning architectures allow platforms to adaptively segment users and dynamically adjust ad content and timing for maximum impact. Real-time bidding (RTB) engines also integrate ML models to predict click-through and conversion rates instantaneously, optimizing ad spend and audience reach. Beyond tactical applications, ML facilitates strategic insights, revealing which channels and messages drive long-term engagement and return on investment (ROI). Conversion prediction further enhances this framework by evaluating not only the likelihood of a single transaction but the potential for ongoing loyalty. However, challenges remain in integrating heterogeneous data sources, ensuring data privacy, and maintaining model transparency. As digital marketing continues to evolve, ML-based systems offer unprecedented agility and accuracy in campaign execution, reshaping how businesses attract, engage, and retain customers.