<p>This meta-analysis synthesizes insights from over 400 peer-reviewed studies and critically examines the transformative impact of deep learning models in peripheral blood smear analysis. This review categorizes and evaluates prominent methodologies like convolutional neural networks, object detection frameworks, and segmentation techniques, emphasizing their significance in classification, counting, morphological analysis, disease detection, and cell segmentation. This analysis indicates significant advancements in automated diagnostic tools, especially in identifying disease in blood cells. Some critical gaps remain, including the limited exploration of differential white blood cell counting, the underdevelopment of automated morphological analysis, and the absence of standardized evaluation criteria. A notable lack of large, diverse, and well-annotated datasets also contributes to the generalizability and robustness of deep learning models. To fully realize the potential of deep learning technologies in improving diagnostic accuracy and clinical decision-making in hematology, these findings underscore the need to address these gaps in future research.</p>