Genome sequence alignment is an essential part of bioinformatics, where the alignment of DNA sequences upon comparison gives an understanding of the relationship between their structural, functional, and evolutionary nature. While classical methods are reliable, they often make the computation demanding, making it difficult to handle, especially when dealing with large datasets. Existing studies try to solve this problem using AI techniques, achieving high accuracy, but they often do so at the expense of increased processing time, which limits their practical applicability for large datasets and real-time implementation. Additionally, they require robust hardware for execution due to the nature of the AI model used, causing it to be less accessible. The proposed work investigates the application of various optimized AI approaches, specifically Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Random Forest (RF) algorithms. The CNN model with the ADAM optimizer achieved an accuracy of 99.97%, emerging as the best model of this study. Although the Bi-LSTM model resulted in a similar accuracy of 99.97%, it required significantly more computational time when compared to CNN. The RF model offered a marginally lower accuracy of 98.8%. In contrast to the existing studies, the proposed work provides a balanced solution by significantly reducing the computational time by 0.25× while improving the accuracy by 0.2–0.7% and lowering hardware requirements. Furthermore, it specifically seeks to improve upon the current research aimed at parallelizing the computational steps within the algorithm to enhance its performance by employing advanced artificial intelligence algorithms.