This paper introduces a hybrid artificial intelligence (AI) system that combines deep learning (DL) and genetic programming (GP) to solve visual arithmetic problems. DL is used for digit recognition in images, while GP performs symbolic reasoning to compute arithmetic results. Four image-based datasets were created, each representing different levels of difficulty in addition and subtraction tasks, with and without carrying or borrowing. By extracting and encoding structured representations from visual data, the new hybrid approach can explicitly capture the symbolic relationships, enabling more efficient reasoning. The findings suggest that the hybrid system matches the accuracy of DL benchmarks, while offering enhanced interpretability and data-efficient generalisation. The proposed approach demonstrates the potential of combining sub-symbolic perception with symbolic reasoning to unify perceptual understanding and logical inference as a foundational step towards human-like artificial intelligence.