Lahore has once again topped the list of the most polluted cities in the world. The situation becomes worse during the winter when the heart of Pakistan faces the "smog season". The transportation and industrial sectors are significant contributors to air pollution, which together hold 68 % share of the total. It is therefore, highly recommended to minimize vehicular and industrial emissions in order to improve air quality. In this study, a straightforward approach is proposed to regulate industrial discharge and traffic throughout the winter. According to estimates, using this technique to the transportation sector, the air pollution can be reduced by 21.5 %. The industrial sector may reduce air pollution by 12.5 % by using a similar strategy. The compound model predicts an overall reduction in air pollution of 34 %.

Machine learning (ML) is a powerful tool in drug design, enabling the rapid analysis of large and complex molecular graphs that represent the structural and chemical properties of medications. It enhances the precision and speed with which molecular interactions are predicted, drug candidates are refined, and potential therapeutic targets are identified. When combined with graph theory, ML allows for the prediction of structural properties, molecular behaviour, and the performance of chemical compounds. This integration promotes drug development, reduces costs, and increases the likelihood of producing effective medicines. In this study, we focus on the efficacy of medications used in the treatment of coronary artery disease (CAD) using graph-theoretical methodologies, such as topological indices. We computed several degree-based topological descriptors from chemical graphs, capturing essential connectivity and structural properties. These variables were incorporated into a machine learning framework to develop predictive models that identify structural factors influencing medication performance. Our study explores a dataset of known CAD drugs using supervised learning techniques to estimate their potential efficacy and support improved molecular design. The findings highlight the utility of graph-theoretical descriptors in enhancing prediction accuracy and providing insights into fundamental structural elements related to drug efficacy. Furthermore, this work emphasises the synergy between chemical graph theory and machine learning in accelerating drug development for CAD, offering a scalable and interpretable framework for future pharmaceutical applications.