Blood cell disorders are a major cause of serious illnesses, including leukemia, anemia, and immune-related conditions. In clinical practice, blood smear analysis remains predominantly manual, relying heavily on the skills and experience of laboratory technicians. However, this traditional approach carries significant risks of diagnostic errors, particularly when dealing with rare cases or cells with ambiguous morphology. To address this challenge, this study introduces an automatic blood cell classification method developed on an advanced deep learning architecture. The model is further enhanced with Explainable Artificial Intelligence (XAI), which provides visual interpretability by highlighting the most critical regions in input images. This important feature supports clinicians by enhancing interpretability and transparency in clinical decision-making. The proposed method was rigorously evaluated on two benchmark datasets, Naturalize 2K-PBC and Microscopic Blood Cell, achieving a classification accuracy of up to 89%. The findings clearly highlight the strong potential of this approach for reliable automated hematological diagnosis, especially in resource-limited healthcare environments.