The seventh most common cause of blindness and visual impairment worldwide especially in East Asian nations, pathological myopia (PM), has a reported global frequency of up to 3%. Myopia that is pathological is accompanied by deteriorating retinal abnormalities, resulting in irreversible eyesight loss if left untreated. Traditional techniques include manually identifying pathological myopia, which has resulted in incorrect diagnoses and total vision loss. In a world population where myopia prevalence is on the rise, early and automated PM identification from fundus images may help avert blindness. Since Deep Learning architecture has attained state-of-the-art performance and has outperformed humans in Computer Vision challenges since 2016, there is very little likelihood of an incorrect diagnosis. The dataset could contain information with incorrect labels when the annotation task is given to non-experts and that might hinder the optimization dynamics of classification models in addition to producing classification models with subpar performance. It imposes the need for an automated segmentation framework that can precisely identify the regions with margins and help ophthalmologists detect and monitor the severity level of myopia early on. A Hybrid Dense-ED-UHI: Encoder Decoder based Unet Hybrid Inception model with 15 fold cross validation for the detection of pathological myopia (PM) is shown in the proposed study. The proposed model was trained and validated using PALM (Pathologic Myopia Challenge) dataset. The model performs with 95.4% dice score, 99.9% accuracy, 99.9% auc, 99.2% sensitivity, and 99.9% specificity for myopia segmentation.