This paper presents a deep learning (DL)-based approach to support malaria diagnosis by detecting both malaria parasites and leukocytes in thick blood smears. The study aims to address the time-consuming and labor-intensive nature of conventional microscopic examination, which is crucial for generating effective prevention and treatment strategies. The authors used a dataset of 333 thick blood smear images, both positive and negative for malaria, and applied data augmentation techniques to increase the dataset size. The YOLOv8 algorithm was used for model training, and the model's performance was evaluated using accuracy, sensitivity, specificity, mAP, and R². The results showed that the model achieved 95% and 98% accuracy in detecting parasites and leukocytes, respectively, with significantly reduced detection time compared to human experts. The model also demonstrated high agreement (93%) with expert measurements in calculating parasite density. The study highlights the potential of AI in improving malaria diagnosis, particularly in areas with limited healthcare resources. However, the authors recommend further validation and refinement of the approach in real-world clinical settings.This paper presents a deep learning (DL)-based approach to support malaria diagnosis by detecting both malaria parasites and leukocytes in thick blood smears. The study aims to address the time-consuming and labor-intensive nature of conventional microscopic examination, which is crucial for generating effective prevention and treatment strategies. The authors used a dataset of 333 thick blood smear images, both positive and negative for malaria, and applied data augmentation techniques to increase the dataset size. The YOLOv8 algorithm was used for model training, and the model's performance was evaluated using accuracy, sensitivity, specificity, mAP, and R². The results showed that the model achieved 95% and 98% accuracy in detecting parasites and leukocytes, respectively, with significantly reduced detection time compared to human experts. The model also demonstrated high agreement (93%) with expert measurements in calculating parasite density. The study highlights the potential of AI in improving malaria diagnosis, particularly in areas with limited healthcare resources. However, the authors recommend further validation and refinement of the approach in real-world clinical settings.