This paper provides a comprehensive review of predictive models of machine learning and its application to forecast customer retention, a critical factor in ensuring the long-term viability and profitability of businesses across diverse sectors. The foundation of successful customer retention prediction rests upon the completeness, comprehensiveness, integrity, and overall quality of the dataset which is utilized as training dataset for the predictive models under consideration. The rapid evolution in the market place demands advanced analytics to optimize campaign strategies. This study explores an AI-driven framework using an 8000-sample dataset to enhance campaign performance. The approach begins with Principal Component Analysis (PCA) for feature selection, effectively simplifying high-dimensional data while preserving key information. An AI model Support Vector Machine (SVM), recognized for its robustness is employed for predictive analytics. The model achieves an outstanding accuracy of 92% with minimal error. These insights empower marketers to predict campaign outcomes with high precision, enabling data-driven optimizations. To evaluate the significance of variations among different strategies and audience segments, Analysis of Variance (ANOVA) is applied. This statistical method validates the findings by identifying meaningful differences in campaign performance across groups, aiding strategic decision-making and resource allocation. The integration of PCA, SVM, and ANOVA creates a cohesive framework for driving impactful marketing decisions. Together, these techniques enable a scalable and practical solution for optimizing digital marketing efforts. The results of this paper emphasize the latent power of AI-powered methodologies to revolutionize marketing analytics by enhancing efficiency and accuracy, which leads to right decision-making. This study provides a roadmap for leveraging data-driven insights to achieve superior marketing outcomes.