Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, poses a significant global health threat. This study leverages artificial intelligence, employing a Modified Learning Principle, for intelligent HCC diagnosis in CT-scanned liver images. Integrating customized and pre-trained neural network components, the hybrid model demonstrates exceptional performance. It achieves an impressive 99.10% accuracy during training, and in testing, it exhibits perfect generalization with 99.74% accuracy. The combination of pre-trained and custom components highlights the model’s versatility and effectiveness across various clinical datasets. The Modified Learning Principle enhances HCC diagnosis by stratifying patients, directing treatment choices, and improving precision in AI-driven medical imaging, thereby combating global cancer-related morbidity and mortality. With further refinement, this model holds potential as a valuable tool for facilitating early detection of hepatocellular carcinoma, benefiting both healthcare professionals and patients.