Smooth Cayenne Pineapple (Ananas comosus L. Merr.) plays a significant role in global agriculture. The Philippines, ranking second in pineapple production, emphasizes its importance in the agricultural sector. This paper assessed the maturity of Smooth Cayenne pineapples using image processing and artificial neural networks (ANNs). Through pixel analysis in Regions A and B, results showed a shift from green to yellow hues indicative of ripening, corroborating physiological changes during maturation. The ANN classification model achieved high accuracies, particularly at maturity indices 3 and 4, signifying its efficacy. Varied accuracies across other indices hint at challenges distinguishing adjacent maturity stages. This study emphasizes how image-based techniques can be used to evaluate fruit maturity, which is crucial for quality assurance. Future research should refine the classification model to address accuracy fluctuations and optimize pineapple maturity classification systems.