BACKGROUND: Scoliosis is a widespread musculoskeletal disorder of bending and twisting of spine. In this medical ailment, spine curves to the side and even in severe cases it can twist and can take several bends. For the diagnosis and treatment of scoliotic patients, the Cobb’s angle is a critical marker of the body’s curvature. OBJECTIVE: There are many researches that have been conducted to automate the manual measurement of the angle and every investigation has their own limita- tions. METHODS: This paper presents a method for precisely measuring the Cobb’s an- gle using deep learning based techniques. Mainly it comprises of feature enhance- ment of augmented dataset, a bespoke code for landmark estimation on the spine, segmentation model based on the U-Net architecture and a custom code for Cobb’s angle measurement. These measured angles are then compared to the given angles for the segmentation on biomedical (X-ray) images. RESULTS: The findings demonstrate that the proposed technique offers an auto- mated and impartial way for precisely measuring this angle with an overall accu- racy of 97% and Root Mean Square Error (RMSE) of 4.99, which can lower the variability, facilitate early detection, accurate diagnosis, and monitoring of scolio- sis progression. Additionally, it aids in the treatment planning and evaluating treat- ment outcomes. CONTRIBUTION: By leveraging the applications of Cobb’s angle measurement, healthcare professionals can enhance the quality of care and can improve long-term outcomes for individuals with scoliosis.