Dr. Sundeep Kumar K, V Bharathi, D. Ramesh, U. Satyanarayana 02 04 Star type classification plays a crucial role in astrophysical research and space exploration Identifying different types of stars helps in understanding stellar evolution examining their physical characteristics and exploring the properties of celestial bodies throughout the universe Accurate classification supports cosmological research improves models of stellar lifecycles and enhances the accuracy of the HertzsprungRussell diagram It also benefits practical applications such as spacecraft mission planning telescopebased observations and largescale astronomical surveys through automated categorization of stars Traditional methods for classifying star types such as statistical techniques and decision trees often fall short in performance These approaches typically struggle with capturing the complex nonlinear relationships present in astronomical data and underutilize available features Moreover manual feature engineering becomes inefficient and impractical when applied to extensive datasets resulting in lower accuracy and reduced generalization to new star types In this work we focus on key features such as temperature luminosity radius magnitude color spectral class and star type labels including Red Dwarf Brown Dwarf White Dwarf Main Sequence Super Giants and Hyper Giants We conduct a detailed evaluation of multiple machine learning ML models for star type prediction and propose an enhanced approach aimed at improving both classification accuracy and computational efficiency Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026