This paper presents Min-Cart, an innovative framework designed to mitigate air pollution and reduce carbon footprints through an eco-friendly methodology. Effective strategies are crucial for sustainable development as urban areas grapple with escalating pollution levels and climate change challenges. Min-Cart integrates advanced data analytics and long short-term memory (LSTM) neural networks to reduce carbon footprints. Transportation optimization, industrial scheduling, and community engagement techniques are designed to assess air quality, forecast pollution trends, and minimization of carbon footprints, enabling timely interventions. By focusing on transportation optimization, industrial scheduling, and community engagement, the Min-Cart framework aims to foster greener practices. Experimental results demonstrate significant reductions in pollutant emissions and carbon output, highlighting the potential of eco-friendly approaches in creating healthier urban environments. Also, the Min-Cart framework achieves 58%, 32%, and 18% better prediction accuracies than SVM, Improved convnet and RNN, and CNN-ILSTM models. This work contributes to environmental sustainability and paves the way for future research in smart city initiatives and climate resilience.