Machine learning (ML) has emerged as a powerful tool in quantum chemistry, offering new ways to accelerate scientific discovery and design processes. This abstract presents an overview of the applications of ML in quantum chemistry, highlighting its impact on the field. ML techniques enable the prediction of molecular properties, such as energy levels and reactivity, accelerating drug discovery and materials design. Quantum simulations can also be expedited using ML, reducing computational costs and enabling the exploration of larger chemical spaces. ML algorithms aid in rational drug design, predicting drug-target interactions and facilitating the identification of potential candidates. Additionally, ML models contribute to reaction prediction and mechanism elucidation, property estimation, and database generation. By leveraging ML algorithms, researchers can extract valuable insights from vast amounts of data, advancing our understanding of chemical systems. Five keywords associated with this abstract include: machine learning, quantum chemistry, molecular properties, drug discovery, and materials design.