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Sequence alignment is a crucial procedure in bioinformatics, facilitating the comparison and study of DNA and protein sequences for evolutionary and functional investigations. The growing amount and complexity of genomic datasets pose considerable computational problems for conventional CPU-based alignment techniques. This study presents an improved parallel computing framework that combines Central Processing Units (CPUs) and Graphics Processing Units (GPUs) with NVIDIA’s Unified Memory (UM) architecture to enhance the efficiency of DNA sequence alignment operations. The suggested method utilizes the extensive parallelism of GPUs while ensuring effective memory management via UM, thus reducing data transfer overhead between the host and the device. Experimental results indicate a significant enhancement in execution speed and computational efficiency relative to traditional sequential methods. The results validate that hybrid CPU-GPU processing, augmented by Unified Memory, offers a scalable and high-performance solution for contemporary bioinformatics applications necessitating intense sequence analysis.