This chapter explores the integration of artificial intelligence, machine learning, and clustering methods within the field of sustainability science. It offers a structured overview of how these technologies support complex problem-solving in areas such as environmental monitoring, waste management, renewable energy, and social behavior modeling. A conceptual framework is presented to clarify the relationships between data analysis, model development, and decision-making processes in sustainability contexts. Particular emphasis is placed on the role of clustering techniques, including comparative insights into their applications across diverse domains. The chapter also addresses major ethical and practical concerns, such as transparency, data access, and the uneven distribution of technological capacity. Finally, future directions are discussed, highlighting the need for collaborative, interdisciplinary approaches and adaptive policy frameworks. The key contribution of this chapter is to connect computational techniques with sustainability goals, offering researchers and decision-makers an integrated perspective on how data-driven tools can help inform effective and inclusive actions for long-term environmental and societal resilience.