Digital platforms have accelerated their evolution which demands increased difficulty in optimizing digital marketing strategies. This study analyzes how Machine Learning techniques combined with Deep Learning methods solve these problems through improvement of Customer Segmentation and Campaign Performance Prediction and Recommendation Systems and Sentiment Analysis and Ad Optimization. This paper examines advanced technologies since they help improve digital marketing operations alongside their difficulties of managing scalability and model interpretability and protecting data privacy. Our study reviews multiple ML and DL approaches through a literature research to show their practical uses in digital marketing such as supervised and unsupervised learning, reinforcement learning and deep neural networks along with generative models. The research establishes that these methods facilitate live optimization while they enhance accurate customer segmentation with personalized content delivery which results in better customer retention and lucrative returns. The paper recognizes emerging trends by focusing on ethical AI as well as adaptive marketing practices. The practical business benefits from this study show that combining ML with DL produces data-based decisions alongside better marketing investment management and higher levels of customer satisfaction. The review presents significant information that assists researchers who study AI marketing innovations and supplies practical implementation guidance to practitioners interested in AI technology adoption.