Predicting sensory mouthfeel from instrumental measurements remains a key challenge in food quality assessment. This study presents an AI and machine learning (ML) tool designed to predict sensory mouthfeel in yogurt formulations using analytical chemistry, rheology, tribology, and texture profile analysis. Standardized instrumental and sensory testing protocols were developed to ensure consistent data collection and improve model accuracy. The AI/ML model was trained on instrumental data alongside sensory panel evaluations, establishing correlations between physical properties and perceived mouthfeel attributes.  By leveraging this approach, the tool enhances lab efficiency by reducing reliance on costly and time-intensive sensory panels while maintaining accuracy in product evaluation. Additionally, the implementation of standardized methods improves reproducibility and reliability in food texture analysis. This presentation will outline analytical instrument method development, data collection strategies, model development, and validation techniques, offering insights into how AI can optimize analytical workflows in food quality testing. The findings highlight the potential for AI-driven methodologies to advance instrumental analysis, providing a scalable and efficient alternative for sensory prediction in dairy and other food systems.