Malaria is a major global health threat, and timely and correct diagnosis is essential for effective treatment. Traditional diagnostic methods, such as the microscopic examination of blood smears, are time-consuming and require expert personnel. The study presents a mix of machine learning methods for automatic diagnosis of malaria by using the feature extraction capability of Convolutional Neural Networks (CNNs) along with the efficient classification performance of traditional machine learning classifiers. For our study, we utilize VGG16 CNN with a weight pre-trained on ImageNet to extract the features from non-infected and infected blood cell images from malaria. Five classical machine learning algorithms, such as Random Forest, Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), & Gradient Boosting, are used to classify the extracted features. Each classifier's performance is calculated based on accuracy, F1 score, precision, and recall metrics. The results of our experiments showed that the hybrid model has high accuracy in classification, where the Logistic Regression classifier could achieve above 93% accuracy. This hybrid method is a powerful diagnostic for malaria disease, accomplishing a more satisfactory compromise between the efficacy of the deep learning architectures such as CNNs, and the computational capabilities of more conventional classifiers. It holds promise for deployment into resource-limited settings where fast, automated threading diagnostic systems are much needed