The convergence of Artificial Intelligence (AI) with food chemistry has emerged as a transformative force, reshaping the landscape of predictive modeling and quality analysis in the food industry. This abstract provides an overview of the key themes and challenges identified through a review of recent research papers in this dynamic field. The application of AI in food chemistry is propelled by its potential to optimize food quality parameters, from sensory attributes to safety and nutritional content. Predictive modeling, leveraging machine learning algorithms, plays a pivotal role in forecasting and enhancing various quality aspects. Challenges, however, abound, starting with the need for high-quality and diverse datasets representative of the complexities within food chemistry. Interpretability and explainability of AI models remain critical for trust, especially in applications impacting human health. Standardization emerges as a crucial requirement to facilitate comparisons across studies. The integration of AI with traditional methods, ethical considerations, and regulatory compliance present ongoing challenges. Computational resource constraints and processing time, particularly with complex models, highlight practical considerations in AI application. Despite challenges, the benefits are vast. AI aids in automating quality control processes, optimizing supply chains, and even personalizing nutrition plans. As the food industry navigates towards increased AI adoption, collaboration between researchers, industry stakeholders, and regulatory bodies becomes paramount.. The dynamic interplay between opportunities and challenges in the integration of AI within food chemistry. As the field evolves, addressing these challenges will be essential for realizing the full potential of AI in shaping a safer, more efficient, and innovative future for the food industry.