A research project fills the support technology deficit concerning assistive technologies for visually impaired Nigerians by creating lightweight Convolutional Neural Networks (CNNs) designed for indigenous food identification. We improved upon a past CNN-based Automatic Food Classification Model (AFCM) called AFCM by introducing our novel Custom Convolutional Fuzzy Neural Network (CCFNN) that features fuzzy logic and benchmark models MobileNet and LeNet-5. The database contains 4,000 images that represent 20 Nigerian swallow foods across various freshness states from fresh to 2-day-old. The images were transformed and resized to 100×100×3 to improve generalization and further enhanced through 30% image transformation methods (rotation, flipping, and zooming). The CCFNN implemented on a Lenovo T480S laptop using an Intel i7 processor and 16GB RAM demonstrated outstanding performance by attaining 95.2% accuracy when no augmentation was used and 80.9% accuracy with augmentation while having fewer than 30K parameters. This outperformed the accuracy results of AFCM that decreased to 37.4% when augmentation was activated. The combination of MobileNet and LeNet-5 operated at high accuracy (~95.7%) with light computing needs while CCFNN utilized fuzzy Gaussian inputs for understandable decisions crucial for assistive use. The study demonstrates CCFNN achieves outstanding results by maintaining an AUC-ROC of 0.9982 without augmentation and 0.9958 with augmentation which exceeds AFCM's 0.9545 with augmentation assessments. The study demonstrated that CCFNN delivered better deployment feasibility because it completed training steps in 850ms while requiring 200 times fewer parameters than MobileNet (4.2M) and running faster training steps than AFCM (998ms). Cultural adaptation of AI systems is needed according to research findings and CCFNN demonstrates strong potential to support mobile services in poor neighborhoods. The future research plan will investigate fuzzy pooling layers together with quantization methods for achieving improved real-time functionality. The proposed research brings inclusive AI by closing the discrepancy between Western-based models and localized requirements while offering sustainable solutions for assistive technology implementation in underdeveloped areas.