Bread staling is a temporally dynamic process driven by starch retrogradation, moisture redistribution and structural disintegration of the crumb matrix. While these changes originate within the hydrated starch-gluten structure formed during baking, they are mostly observed as deterioration in texture and appearance during storage. Conventional staling assessment methods like rheological, thermal, moisture and sensory tests are often destructive, time-consuming and limited in capturing the multidimensional, time-dependent kinetics of bread crumb texture deterioration. This study introduces a multitarget machine learning (ML) framework integrating Texture Profile Analysis (TPA), image-based crumb structure analysis and Hunter LAB colour parameters (L*, a*, b*) for prediction of bread shelf-life and texture decay. A dataset of 350 bread crumb samples from five commercial brands stored over seven days at ambient temperature was analyzed. TPA parameters (14) including hardness, cohesiveness, gumminess, chewiness, resilience and springiness were measured using the Texture Analyzer, and day-wise bread slice images were captured for crumb structure. Crumb structure and appearance were characterized through image analysis, while colour parameters to capture time-dependent visual deterioration associated with staling. A Multi-Output Random Forest Regressor was trained to model the nonlinear progression of textural attributes, yielding R² values of 0.99 (hardness), 0.96 (chewiness), and 0.94 (gumminess). Extrapolative modelling for days 8 to 10 further confirmed the model’s generalizability. An improved Grey Wolf Optimization algorithm was applied for image feature selection, using an Ensemble Random Forest model to identify the top 10 features. The resulting multimodal system, comprising 14 TPA variables, optimized image features, and Hunter LAB parameters, achieved over 98% accuracy in predicting storage period and revealed strong correlations between the crumb structure, colour degradation and textural features. The proposed method enables non-destructive, scalable and reliable estimation of bread staling and shelf-life estimation for intelligent food quality monitoring and lays the groundwork for quality assessment tools based on AI and ML in commercial bakery operations.