The project begins with a literature review of the complete NASA Near-Earth Object (NEO) database using new data science and machine learning techniques to gain insights into the intrinsic patterns and behaviors of Near-Earth Objects. The objective of the research is to make a systematic and scientific contribution to the current understanding of the objects and their potential impact on Earth. With the interpretation of NEO traits largely of concern, prediction of possible risk, and generation of interactive visualizations, our work entails significant steps such as thorough data preprocessing, extensive exploratory data analysis, machine learning model training, risk prediction, and interactive visualization. With an unexpected turn of events, our method employs novel data preprocessing for uniformity as well as latent dimension recovery between NASA NEO data. Exploratory exploration aids in discovering buried patterns and trends. Attention is devoted to the dynamical distribution, orbital intricacies, and temporal change of NEOS, breathing life into the starry landscape. The backbone of machine learning innovation is model training, with algorithms being tuned but not just that, honed to unprecedented accuracy from the wisdom of the past. With such far-out models in action, they predict impending threats wrapped in NEOS, paving the way for an era of astro threat prediction. Our figures transcend the ordinary, painting a mural cosmic of spatial and temporal distributions by an art brush, in a forceful assertion of striking figures of hazard predictions.