Modern marketing funnels often suffer substantial customer drop-offs at key stages, limiting both retention and revenue potential. This study formulates and empirically validates a predictive analytics framework that leverages advanced machine learning algorithms, behavioral analysis, and real-time event segmentation to proactively identify and mitigate high-risk drop-off points in marketing funnels. Multi-channel user engagement data is integrated and continuously monitored to train and validate classification models—Gradient Boosting Machines, Random Forests, and XGBoost—that predict drop-off probabilities at each funnel stage. These insights trigger automated, personalized interventions, including targeted emails and retargeting campaigns, with continuous A/B testing to optimize efficacy. Empirical validation using anonymized datasets from the SaaS, e-commerce, and fintech sectors demonstrates marked improvements post-implementation: funnel conversion rates increased from 28% to 34%, while key stage drop-off rates fell by nearly 29% and lead engagement indices saw a 32% uplift. The framework is designed for operational scalability, cross-industry applicability, and data privacy compliance, addressing persistent research gaps regarding real-time adaptation and holistic funnel management. By linking predictive modeling directly to actionable marketing strategies, the study demonstrates how organizations can transition from reactive to anticipatory funnel management, maximizing marketing ROI through dynamic, data-driven engagement. These results contribute new knowledge for practitioners and researchers seeking systematic approaches to optimize the customer journey and foster sustainable business growth.