Stroke is a critical condition marked by the death of brain cells due to inadequate blood flow, necessitating improved predictive models for stroke lesions. The accuracy and flexibility required to forecast and classify stroke lesions is lacking in current approaches, which compromise patient outcomes. To solve these issues, Bille-Viper-Segmentation with the Tandem-MU-Net Model is suggested as a solution for tissue damage detection problems. This study improves blood flow detection in stroke images by introducing the Bille-Viper-Segmentation method to overcome difficulties in recognizing tissue injury. This novel method effectively samples pixel data and analyzes fogging phases related to stroke lesions by utilizing a Deep Luxe Gauging Tree. Existing methods struggle with flexibility in varying conditions; thus, the Trans-Lucent-Rich Reprise Pattern recognition algorithm for precise identification of infected areas is introduced. Furthermore, the Focus View Algorithm is suggested, which incorporates features from infarcted regions to improve early detection of emerging lesions. Furthermore, the Tandem-MU-Net model is used to extract essential morphological features and categorize stroke types, including Hemorrhagic and Acute strokes, through an investigation of their neutral and ionic forms. The results show that the suggested model performs substantially better than existing methods, achieving an amazing accuracy rate of 75%, recall rate of 83%, F1 score of 98%, Dice score of 98%, and precision of 73%, all while operating effectively in a time frame of 250 seconds.