This study explores advancements in sequence alignment algorithms, focusing on DQNalign, EdgeAlign, SAdLSA, and DeepFam. With the advent of next-generation sequencing (NGS), the demand for efficient alignment techniques has surged. Traditional methods like the Greedy X-Drop Algorithm have limitations, prompting the development of heuristic approaches. DQNalign leverages deep reinforcement learning to achieve linear complexity, while EdgeAlign employs reinforcement learning for efficient alignment on edge devices. SAdLSA utilizes deep learning to align protein sequences based on learned folding codes, demonstrating superior performance even in the ‘twilight zone’ of sequence identity. DeepFam introduces an alignment-free approach for protein function prediction, showcasing high accuracy and scalability. Comparative analyses highlight the strengths and limitations of each algorithm, providing insights into their practical applicability in computational biology tasks. Additionally, the chapter concludes with a comprehensive comparison between these algorithms, elucidating their respective advantages and disadvantages in various biological and computational contexts.