The precise identification and categorization of white blood cells (WBCs) hold paramount importance in the realm of diagnosing and monitoring diverse medical conditions. Manual WBC identification and classification procedures are not only time-intensive but are also susceptible to human errors. This study proposes an automated methodology for WBC identification and classification by harnessing advanced machine learning techniques. The devised system capitalizes on an annotated dataset of WBC images, which serves as the foundation for training a machine learning algorithm. Prior to analysis, the images undergo a pre-processing stage to enhance their quality and eliminate noise artifacts. A repertoire of feature extraction techniques is subsequently deployed, encompassing the extraction of pertinent attributes such as shape, texture, and color characteristics from the images. The ramifications of this automated approach are profound, resonating across an array of medical applications. Specifically, the automated identification and classification of WBCs through machine learning algorithms wield substantial potential in aiding medical practitioners in diagnosing an assortment of conditions. This encompasses infections, leukaemia, immune disorders, and beyond. Furthermore, the proposed system introduces an avenue for expediting the analysis process, culminating in prompt and precise outcomes. In effect, this contributes significantly to elevating the quality of patient care and ultimately enhancing healthcare outcomes.