Our heart pumps red blood constantly, carrying waste products, hormones, nutrition, oxygen, and other essential health markers. Blood cells in this fluid are made up of many components, including platelets, RBCs, and WBCs. Neutrophils, Lymphocytes, Monocytes, Eosinophils, and Basophils are further subdivided into WBCs. To precisely identify and treat numerous kinds of blood-related illnesses, the detection and analysis of these blood cells is necessary for advanced diagnostics in medicine. Convolutional neural networks (CNNs), for example, are highly relied upon in modern medical diagnostics for the examination of blood cells. The precision and potency of blood cell identification procedures have been greatly improved by CNNs and Additional approaches to deep learning like RNNs and YOLO. The persistence of this research is to list challenges in White Blood cell detection and classification problem. Deep learning-based blood cell identification and classification approaches based on Single Stage approaches. Single Stage Approach involve You only look Once (YOLO). It explores the various architectures for feature selection and classification starting from YoloV1 toV5 and it’s various modifications. In this paper would be looking at things like network parameters, training procedure, and transfer learning application for each variation. It also assesses model complexity, accuracy, performance, generalization, computational efficiency, and transfer learning capabilities, in addition to variables like data amount and source.