Diabetic retinopathy is one of the most devastating complications associated with diabetes and is a major global health issue due to risk that it has to cause blindness in affected individuals if not well managed. DR can be considered as a disease that has severe outcomes, which is why its early and accurate diagnosing is essential. This work provides an insight based on machine learning methodologies for DR detection accompanied with the use of state-of-the-art deep feature extraction methods. The strategy entails the use of a combination of classifiers such as Decision Trees, Random Forests, Support Vector Machine among others with a view of boosting the detection rates. In the same vein, deep learning models which include MobileNetV2, DenseNet121, and InceptionResNetV2 are used as feature extractor from the retinal images. The classifiers are optimized hyper parameters to ensure that they get to offer the best performance. In terms of data pre-processing, the study uses a broad range of data augmentation and dataset standardization. Such findings corroborate the effectiveness of this half-breed model in enhancing DR identification precision and providing a novel approach for enhancing early healthcare solutions and patient’s quality of life.