Early detection of pulmonary nodules is crucial for improving lung cancer prognosis. Deep learning (DL) algorithms have emerged as promising tools for automated nodule detection in chest CT scans, offering potential to enhance diagnostic accuracy and reduce radiologist workload. This review synthesizes current literature (2020 2025) on DL-based pulmonary nodule detection, focusing on algorithm performance, clinical impact, and implementation challenges. Across multiple studies, DL systems, particularly 3D convolutional neural networks, U-Net architectures, and hybrid models integrating detection and classification, demonstrated high sensitivity (79–95%) and variable false-positive rates, often matching or exceeding radiologist performance. DL as a second reader improved detection of small, subsolid, and subtle nodules, while segmentation modules facilitated volumetric assessment for follow-up and malignancy risk estimation. Despite these advances, limitations remain, including dataset heterogeneity, inconsistent evaluation metrics, elevated false positives in some models, and limited prospective evidence on clinical outcomes. Future research should emphasize multi-center validation, standardized reporting, workflow integration, and assessment of patient-level outcomes. With ongoing technological refinement and rigorous evaluation, DL algorithms hold the potential to become an integral component of chest CT interpretation, improving early lung cancer detection and optimizing clinical decision making.