Today’s digital era fast development of AI platform which is very beneficial to company to predict client engagement prediction the client engagement. Machine learning ability to do Client Engagement and Predictive analysis and enhance consumer interaction is being revolutionized by the fast development of AI Techniques. This research recommends developed a analytical architecture that augments client engagement analytics by incorporate Convolution Neural Networks (CNN) with Support Vector Machines (SVM) and hybrid of CNN and SVM. In the proposed implementation model the system goes through extensive pre-processing and feature extraction using datasets that pertain to demographics, behaviours, and interactions. And the methodology takes advantage of convolution neural networks (CNNs) for accurate engagement prediction and support vector machines (SVMs) for strong classification capabilities by mining consumer data for deep behavioral and interaction aspects. Prior to training the model, When compared to separate CNN and SVM models, the hybrid CNN-SVM method routinely achieves better results in all metrics measured, including accuracy, precision, recall, and F1-score. The results of this study back up the idea that companies can use scalable predictive insights from hybrid learning models to create personalized engagement strategies in real time. In addition to adding to what is known about AI-driven customer analytics, this study solves the issues that come up with data-driven digital marketing and client relationship management.