Accurate malaria diagnosis is essential for global health management, particularly in regions where the disease causes endemic epidemics. The conventional diagnostic approach of blood sample analysis by microscopy requires skilled assessors and is susceptible to human interpretative errors. This study employs an innovative deep learning approach that integrates the VGG-19 and ResNet50 models for the automated identification of malaria through the analysis of blood smear images. The suggested approach enhances prior research by integrating transfer learning with fine-tuning strategies, improving classification accuracy. The data collected from Kaggle is subjected to preprocessing, which includes picture scaling, noise reduction, and data augmentation to enhance generalization. Model convergence efficiency is attained using sparse categorical cross-entropy with the Adam optimizer. Early stopping prevents the model from overfitting, whereas precision, recall, F1-score, and accuracy are used for validation. VGG-19 exhibits enhanced performance after fine-tuning compared to other designs, as demonstrated by experimental data, due to its improved precision and stability. This study enhances medical AI by discovering deep learning applications for malaria detection, which reduce the need for manual diagnosis and expedite illness identification in resource-limited medical environments. Interpretability strategies enhance the clinical use of automated systems and foster confidence between healthcare practitioners and these technologies.