To develop and validate a comprehensive analytical framework for strawberry maturity authentication by integrating artificial intelligence with spectroscopic and chemical analyses, addressing the critical need for objective and automated quality assessment in the food industry's increasingly complex supply chains. We analyzed 90 strawberry samples across three maturity stages using a multi-modal approach. Near-infrared spectroscopy (400-1700 nm) data was processed through 1,220 preprocessing pipelines to optimize spectral analysis. Chemical analysis included titratable acidity and total dissolved solids measurements. For visual assessment, 270 high-resolution images were collected and analyzed using three state-of-the-art deep learning architectures: RegNet_Y_800MF, RegNet_Y_8GF, and Swin Transformer V2, with comprehensive data augmentation techniques. The optimal NIR preprocessing pipeline achieved a Silhouette score of 0.5744 using localized standard normal variate with seven windows. The Swin Transformer V2 model demonstrated perfect classification accuracy (100%), outperforming traditional RegNet architectures while maintaining computational efficiency. Chemical analysis revealed the Brix-to-acidity ratio as the most reliable maturity indicator (4.92±0.80 immature, 6.18±0.98 semi-mature, 8.12±2.40 mature). Principal component analysis of preprocessed NIR spectra showed strong correlations with both visual and chemical properties, validating the multi-modal approach. This research advances food authenticity testing by providing a rapid, non-destructive method for objective maturity assessment. The validated multi-modal approach offers superior accuracy compared to traditional single-method analyses, supporting both producers and regulators in ensuring product quality and preventing fraud. The developed framework demonstrates broad applicability across the fresh produce industry, contributing to improved food safety and quality control while reducing food waste through precise maturity determination.