Application of Machine Learning in Digital Marketing and its Optimization Modeling in Precision Advertising Delivery—Dynamic optimization based on K-Means clustering algorithm and Q-Learning algorithm

With the rapid development of digital marketing, precision ad placement has become a key strategy to improve advertising effectiveness. This paper explores the application of machine learning in digital marketing, especially its optimization role in precise ad placement. The article analyzes four core areas: segmenting user groups through K-Means clustering algorithm, personalized ad recommendation with collaborative filtering technology, ad click rate prediction using logistic regression model, and dynamic optimization of ad placement budget through Q-Learning algorithm. Combining these machine learning methods, advertisers can build more intelligent and personalized advertising strategies based on user behavioral data and advertisement effect feedback, thus realizing precise advertisement placement and maximizing benefits. However, issues such as data privacy, algorithmic fairness, and technical scalability remain challenges in digital marketing. In the future, with the popularization of 5G and IoT technologies, the role of machine learning in ad placement will be further strengthened to provide advertisers with more intelligent solutions.