In the context of a growing global population, an automated framework for disease detection can greatly assist medical professionals in diagnosing ocular diseases. This framework offers advantages such as accurate, stable, and rapid outcomes, thereby enhancing the success rate of early disease detection. The primary objective of this study was to enhance the quality of fundus images using an adaptive contrast enhancement algorithm (CLAHE) and Gamma correction as preprocessing techniques. CLAHE is employed to heighten the local contrast of fundus images, while Gamma correction enhances the intensity of relevant features. This research adopts a deep learning approach that integrates convolutional neural networks (CNNs) and both short-term and long-term memory (LSTM) mechanisms. The purpose of this combination is to automatically detect aged macular degeneration (AMD) in fundus ophthalmology. In this mechanism, CNNs are utilized to extract pertinent features from the images, and LSTM is subsequently employed to discern these extracted features. To validate the effectiveness of the proposed method, a diverse dataset of 2000 experimental fundus images is collected from various sources. These images are categorized into four distinct classes in an equitable manner. Quality assessment techniques are then applied to this dataset. The hybrid deep AMDNet23 model proposed in this study achieves successful detection of AMD ocular disease with an achieved accuracy of 96.50 %. Moreover, the system's prowess is benchmarked against 13 other pre-trained CNN models, effectively illuminating its supremacy in AMD ocular disease diagnosis within the realm of fundus imagery datasets. This exhaustive comparison underscores the method's immense potential and reaffirms its position at the forefront of cutting-edge ocular