The merger of AI, ML, and sustainability offers an excellent solution for addressing pressing issues related to the environment and natural resources. This paper combines computational algorithms and fundamental mathematical principles to develop sustainable solutions to problems. From a computational perspective, classification, clustering, and reinforcement learning ML algorithms are applied for data mining, decision making, and automation in various industries, including energy, agriculture, and city planning. On a mathematical front, the study identifies optimization, probability theory, and statistics as the most important concepts that can help increase the accuracy, effectiveness, and reliability of AI/ML algorithms. Mathematical analysis helps us comprehend how AI/ML algorithms work, minimize errors, and generalize their results in different scenarios. It is also worth mentioning the practical implementation of AI and ML solutions, including resource management, environmental surveillance, and predictive climate modeling. This analysis also addresses several challenges such as computational demands, data quality issues, and ethical concerns. In conclusion, this article highlights the importance of combining AI/ML techniques with mathematics to develop solutions that can meet societal needs in the future.
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