Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans. A retrospective analysis of 677 PET/CT scans from a medical center was conducted. The dataset was divided into training (88%) and testing (12%) sets. The DLA-3D model was employed for high-resolution representation learning of cardiac CT images. Data preprocessing techniques were applied to normalize and augment the images. Performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity and p-values. The AI model achieved an average AUC of 0.85 on the training set and 0.80 on the testing set. The model demonstrated expert-level performance with a specificity of 0.79, a sensitivity of 0.67, and an overall accuracy of 0.73 for the test group. In real-world scenarios, the model yielded a specificity of 0.8, sensitivity of 0.6, and an accuracy of 0.76. Comparison with human experts showed comparable performance. This study developed an AI method utilizing DLA-3D for automated CAC detection in non-gated PET/CT images. Findings indicate reliable CAC detection in routine PET/CT scans, potentially enhancing both cancer diagnosis and cardiovascular risk assessment. The DLA-3D model shows promise in aiding non-specialist physicians and may contribute to improved cardiovascular risk assessment in oncological imaging, encouraging additional CAC interpretation.