Machine learning (ML) has transformed the way digital advertising is done and analyzed by social media data. This paper explores ML in targeted advertising, including techniques such as supervised learning, neural networks, and NLP. While ML improves campaign precision and consumer engagement, it also presents challenges such as algorithmic bias, data privacy concerns, and computational scalability. This study is a synthesis of existing research and explores real-world applications, providing a critical analysis of ML’s capacity to optimize social media advertising. It argues that while ML may provide exceptional possibilities for customization and engagement, its success can only be ensured through appropriate ethical practices, transparency, and innovation in technology.