Electronic marketing is fast changing with the convergence of artificial intelligence (AI) and big data analytics, and this allows firms to maximize strategy through real-time insights. The current research proposes an AI-facilitated data analytics system that is expected to improve predictive marketing strategies within digital environments. The approach includes data collection, feature extraction, deployment of models, predictive analytics, and decision-making support, where machine learning procedures like clustering, classification, and deep learning improve customer behavior predictions and campaign improvement. Results indicated notable improvements across the board with a 12.4% boost in the accuracy of customer segmentation, a 12.7% increment in conversion forecast, and an improvement of 15.1% in the response rates in campaigns. The architecture also attained a 28% decrease in prediction latency and a 21.5% improvement in ROI. In summary, the proposed architecture improves predictive marketing abilities, with improved targeting and resource allocation. Future research will address ethical issues, including data privacy and AI bias, as well as enhancing scalability and model interpretability.