Metabolic pathways, which encompass the series of chemical responses within a cell, are critical for understanding natural processes similar to energy product, growth, and cellular form. This explores the crossroad of metabolic pathway analysis with artificial intelligence (AI), an emerging tool that is transubstantiating the field of biology and biotechnology. AI still marks a shift in how these pathways are studied and modeled. The foundations of AI, particularly its use of machine literacy and data-driven algorithms, offer important tools for prophetic modeling of metabolic pathways. Through AI-grounded models, experimenters can pretend and read metabolic responses more directly, furnishing perceptivity into complaint mechanisms, medicine discovery, and metabolic interventions. This further explores how metabolomics data, which involve the large-scale study of small motes within cells, benefit from AI’s advanced analysis ways. AI not only enables the processing of vast datasets but also reveals intricate patterns and correlations that help consolidate our understanding of metabolism in a molecular position. In addition to perfecting exploration styles, AI has opened new possibilities for individualized drug and nutritive interventions.