This research describes an integrated methodology for automated glaucoma identification that makes use of retinal fundus imaging and advanced computational algorithms. The research begins with the diligent collection of a diversified Retinal Fundus Image Database, which includes both normal and glaucomatous cases. Image quality is improved by pre-processing techniques such as Median Filtering and pixel normalization, which are then followed by feature extraction utilizing Local Binary Pattern (LBP) to capture key glaucoma patterns. The use of a Long Short-Term Memory Convolutional Neural Network (LSTM CNN) refines the analysis even further by incorporating spatial and temporal data. Based on learning characteristics, a Support Vector Machine (SVM) classifier improves classification precision. The model's efficacy is evaluated using performance metrics such as accuracy and sensitivity, with segmentation algorithms refining the analysis and concluding in a glaucoma-detected image. This strategy appears to be promising for effective glaucoma screening, early intervention, and vision preservation in at-risk groups.