The scientific community has been captivated for decades by the search for a planet beyond Earth that is suitable for habitation. Exploring the intersection of recent Artificial Intelligence (AI) advancements and exoplanet research, this paper investigates the accelerated identification of habitable exoplanets, tracing its historical development and underlying motivations. Drawing upon a range of strategies including Exoplanet detection, habitable zone analysis, AI-based data analysis, and robotic exploration, this paper employs AI techniques to identify prospective habitable exoplanets, evaluating key factors for planetary habitability while encompassing methodologies like exoplanet characterization and AI-driven observational strategies. The study encompassed several essential analyses, including radial velocity analysis for both Kepler and Mars, modelling the habitable zone around stars in the case of Kepler 542-b, determining suitable liquid water conditions on Kepler 542-b, analysing atmospheric composition for general exoplanet potential (as well as specifically for Mars), estimating surface temperatures along with mass and size on Mars, employing a machine learning algorithm using SVM for classifying microorganisms on Kepler 452-b, utilizing Bayesian inference to create probabilistic models considering temperature, magnetic fields, and radiation levels (infrared, X-rays, gamma rays) on Mars. Additionally, a regression model was developed to predict potential life on Mars by 2050. Constant values were used due to limited data availability. Nevertheless, these findings lay a solid foundation for future research endeavours and explorations in our quest to find liveable planets. It emphasizes the importance of ongoing research and its role in preparing for future explorations and potential colonization beyond Earth.