<p>The combination of deep learning (DL) with cardiac single-photon emission computed tomography (SPECT) marks a groundbreaking development in the field of nuclear medicine. This systematic review evaluates the role of DL algorithms in enhancing cardiac SPECT image quality, optimizing acquisition/reconstruction, reducing noise, and improving clinical diagnosis of cardiovascular diseases. A review of 49 papers identifies convolutional neural networks (CNNs), U-Net models, and generative adversarial networks (GANs) as the leading techniques employed for SPECT image processing. DL applications in myocardial perfusion SPECT imaging demonstrate its capacity to extract complex patterns, enhance spatial resolution, reduce artifacts, and improve diagnostic accuracy for myocardial perfusion assessment and cardiac event prediction. Additionally, DL offers potential for faster imaging workflows and reduced radiation doses. However, challenges such as clinical data heterogeneity, lack of standardized protocols, limited sample sizes, and the need for explainable AI models hinder widespread clinical adoption. Achieving clinical reliability requires interdisciplinary collaboration among clinicians, data engineers, and regulators to validate algorithms, address ethical concerns, and secure approvals. Future directions include integrating DL with real-time imaging for dynamic cardiac monitoring, leveraging multimodality data (e.g. SPECT with CT, MRI, or echocardiography) to develop holistic models, and creating personalized frameworks based on patient-specific factors like age, gender, and medical history. These innovations promise to revolutionize diagnostic precision, enable targeted therapies, and mitigate the global burden of cardiovascular diseases.</p>