Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms, primarily driven by KIT or PDGFRA mutations. While tyrosine kinase inhibitors (TKIs), such as imatinib, have transformed treatment, resistance remains a significant challenge. Alternative TKIs offer options but with increased toxicity and inconsistent efficacy. This study investigates artificial intelligence-driven radiomics for predicting primary TKI resistance in GIST patients. Sixteen patients were classified as imatinib-sensitive or -resistant based on CT scans taken at baseline and one year post-treatment. Expert radiologists manually segmented CT scans and extracted the volume of interest, composed of the principal mass and metastasis. A total of 93 radiomic features were extracted from each axial slice from the axial view. A genetic algorithm was implemented for feature selection and model hyperparameter optimization. The Support Vector Classifier demonstrated the highest accuracy ( \(95\%\) ), underscoring the potential of AI-driven radiomics to guide personalized GIST treatment.