When people think of Data Structures and Algorithms (DSA), they often associate them primarily with software engineering and coding interviews. However, DSA is a fundamental pillar across various domains, including Machine Learning and Artificial Intelligence. This session explores how DSA concepts like trees, graphs, and heaps, as well as algorithmic techniques such as Divide and Conquer, Greedy Algorithms, and Dynamic Programming, are deeply integrated into the foundations of ML. From enabling efficient data handling to optimizing learning processes, DSAs power critical components of ML systems. Through this walkthrough, we’ll uncover how DSAs, beyond their software engineering applications, are pivotal to building and advancing intelligent systems in the AI world.