Precision marketing leverages data science to deliver highly personalized customer experiences, significantly enhancing engagement and marketing efficiency. This review synthesizes academic research and industry case studies from 2018 to 2024, critically analyzing the evolution and current landscape of precision marketing. We focus on advanced machine learning techniques, including clustering, predictive modeling, and real-time decision frameworks, to elucidate their roles in optimizing customer segmentation and campaign personalization. Key challenges, such as data privacy, scalability, and algorithmic bias, are evaluated alongside emerging solutions like federated learning and bias-mitigation strategies. Our analysis demonstrates that hybrid ensemble frameworks, integrating diverse machine learning models, outperform single-model approaches by 12–25% across key marketing metrics, such as conversion rates and customer lifetime value, while addressing ethical considerations. We propose and validate a novel hybrid ensemble framework that achieves a 30% improvement in conversion rates in a controlled e-commerce experiment, effectively balancing predictive accuracy, computational scalability, and regulatory compliance. Future research directions, including the integration of automated machine learning (AutoML) and causal inference within ensemble models, are outlined to address current limitations and enhance interpretability and fairness in precision marketing systems.