Artificial intelligence (AI) has revolutionized various scientific domains, and its application in biochemical data analysis is no exception. This paper explores the integration of AI techniques in biochemical research, highlighting the opportunities and challenges associated with this paradigm shift. By leveraging machine learning, deep learning, natural language processing, and reinforcement learning, AI offers enhanced data interpretation, automation of complex tasks, and personalized medicine. However, challenges such as data quality, model interpretability, computational resources, and ethical concerns persist. Through a comprehensive literature review and analysis of AI applications in protein structure prediction, genomics, metabolomics, drug discovery, and clinical biochemistry, this paper provides insights into the current state and future potential of AI in biochemical data analysis. The results demonstrate the superior performance of AI-driven methods compared to traditional techniques, emphasizing the need for continued research and development in this field.