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Computer intelligence, particularly Artificial Intelligence (AI), has become a cornerstone in advancing bioinformatics. This study aims to explore the role of AI in addressing the challenges of analyzing complex biological data, especially in genomics, proteomics, and metabolomics. Using machine learning (ML) and deep learning (DL) algorithms, AI efficiently processes large-scale data, accelerates genomic research, predicts protein structures, and identifies disease biomarkers. However, challenges such as data quality, computational limitations, and privacy issues remain barriers to its implementation. The findings of this study highlight the importance of continuous innovation, multidisciplinary collaboration, and strict regulations in AI applications. In conclusion, AI holds great potential to revolutionize bioinformatics, significantly impacting scientific research and the development of global healthcare systems.

In recent decades, artificial intelligence (AI) has significantly advanced and shown great potential across various fields, including bioinformatics. This paper examines current trends in AI applications within bioinformatics, highlighting future potentials and the challenges of integrating these technologies. The research utilizes secondary data collection from scientific literature, books, conference reports, and official documents on AI and bioinformatics, sourced from reputable databases like Scopus, IEEE, PubMed, and Google Scholar. Through comparative analysis, similarities, differences, and technological advancements were identified and discussed. Descriptive narrative interpretation was employed to provide a holistic view of AI trends and potential in bioinformatics. Key findings indicate that AI, particularly machine learning and deep learning, is instrumental in genomic data analysis, protein structure prediction, drug discovery, and clinical bioinformatics. Furthermore, the study underscores the benefits of AI in enhancing data analysis accuracy and efficiency, while addressing ethical and technical challenges. Future prospects emphasize the importance of interdisciplinary collaboration to fully leverage AI's capabilities in bioinformatics.