Retinal disorders are of paramount importance in human life and if they do not receive treatment for retinal disorders, humans may undergo visual complications. The physician may diagnose more easily with optical coherence tomography (OCT) in ophthalmology, where early diagnosis is essential. To date, artificial intelligence has recently been implemented more and more frequently in the field of health thanks to technological advancements. In this study, a hybrid Retinal Fine Tuned Convolutional Neural Network (R-FTCNN) has been proposed for the detection of retinal diseases such as diabetic macular edema, drusen, and choroidal neovascularization from OCT images. In this study, the newly constructed R-FTCNN architecture and principal component analysis (PCA) are utilized concurrently as part of this methodology. PCA was used to convert the fully connected layers of the R-FTCNN to principal components, and the Softmax function was applied to the principal components to construct a new classification model. By creating various feature arrays from fully connected layers of R-FTCNN, ablation test was applied and FC layer (or combinations) with the best performance was determined. Afterwards, the FC layer with highest performance was used as the basis for the proposed methodology. Leveraging OCT datasets from Duke and California San Diego University (UCSD), which are often cited in the literature, the performance of the suggested technique was assessed. This technique had 100% accuracy, 100% sensitivity, 100% specificity, 100% precision, and 100% F1 score metric for classifying retinal disorders in the Duke dataset. Furthermore, this technique demonstrated 99.70% accuracy, 99.70% sensitivity, 99.90% specificity, 99.70% precision, and 99.70% F1 score metrics when classifying retinal disorders in the UCSD dataset. ROC curves were generated for the performance summary of the proposed method on two OCT datasets, and the area under the curve (AUC) metrics revealed that the suggested approach performed well in terms of classifications. In this study, pre-trained CNN architectures namely GoogLeNet, AlexNet, and ResNet (18, 50 and 101) were employed to compare the performance of the proposed technique. The proposed method outperformed state-of-the-art technology when conclusions from two OCT datasets were compared with those from past investigations. In conclusion, the proposed framework might aid in the creation of computer-aided diagnostic instruments for the early detection of retinal disorders in ophthalmology.