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White Blood Cells (WBCs) Play A Vital Role In The Immune System, And Abnormalities In Their Morphology And Distribution Are Key Indicators Of Blood Disorders Such As Leukemia. Early And Accurate Detection Of Leukemic Cells Is Critical For Effective Treatment And Improved Patient Outcomes. Traditional Manual Examination Of Blood Smears Under A Microscope Is Time-consuming, Prone To Human Error, And Requires Significant Expertise. To Address These Limitations, This Study Proposes An Automated System For Classifying Leukemic Blood Images Using Deep Learning And Image Processing Techniques. The Approach Involves Preprocessing Of Microscopic Blood Images To Enhance Quality, Followed By Segmentation Methods To Isolate WBCs From The Background. Feature Extraction Is Then Performed To Capture Both Morphological And Texture Characteristics. A Convolutional Neural Network (CNN) Model Is Employed To Automatically Learn Discriminative Features And Classify WBCs Into Normal Or Leukemic Categories. The Proposed Method Aims To Achieve High Accuracy, Robustness, And Efficiency In Leukemic Cell Detection, Thereby Supporting Hematologists In Clinical Diagnosis. This Research Highlights The Potential Of Integrating Deep Learning With Image Processing To Develop Computer-aided Diagnostic Tools For Leukemia Screening.