Introduction: In this day and age, Machine Learning in clinical imaging introduces an energizing time with reengineered and rethought clinical abilities. Deep Learning aids deep further to make physicians feel like a walk in the park with a handful of more desirable resources. Aim and Objective: The Research focuses to classify and segment the abnormalities in the fetal brain MRI images. The normal and lesion tissue are identified with their location from the given raw images. Method: The model will perform localization, Segmentation, and Enhancement of the Fetal Brain and able to address the two significant abnormalities such as Encephalocele and Arteriovenous Malformation using the Improved Semantic Blend Segmentation Algorithm. Results: The model has been trained with the capability to segment the Region of Interest (ROI) on an average of 7.2 Seconds per input. Conclusion: The raw fetal brain images are segmented and enhanced with various classes of input and the results are analyzed which outperforms the existing techniques by saving time and achieving better accuracy.