Sustainable Business Intelligence (BI) integrates sustainability principles into traditional BI practices to support environmentally and socially responsible decision-making while driving long-term growth. This review explores the concept of Sustainable BI, its key components, integration of advanced tools, case studies, best practices, challenges, and future trends. Sustainable BI involves using BI principles to support sustainable business practices, including environmental stewardship, social responsibility, and economic viability. Advanced tools such as predictive analytics, machine learning, IoT integration, and blockchain technology enhance Sustainable BI capabilities, enabling data-driven decisions that optimize resource usage, reduce waste, and improve sustainability performance. Case studies of companies like Procter & Gamble, Microsoft, and Unilever demonstrate successful strategies and outcomes achieved through Sustainable BI. Best practices for Sustainable BI adoption include leadership commitment, cross-functional collaboration, and ethical considerations. Challenges include data privacy, scalability, and the need for continuous improvement and adaptability. Emerging technologies such as AI, IoT, and blockchain are shaping the future of Sustainable BI, offering new capabilities for sustainability performance. In conclusion, Sustainable BI represents a holistic approach to business intelligence that aligns with sustainability principles and offers opportunities for long-term growth and positive societal and environmental impact.Sustainable Business Intelligence (BI) integrates sustainability principles into traditional BI practices to support environmentally and socially responsible decision-making while driving long-term growth. This review explores the concept of Sustainable BI, its key components, integration of advanced tools, case studies, best practices, challenges, and future trends. Sustainable BI involves using BI principles to support sustainable business practices, including environmental stewardship, social responsibility, and economic viability. Advanced tools such as predictive analytics, machine learning, IoT integration, and blockchain technology enhance Sustainable BI capabilities, enabling data-driven decisions that optimize resource usage, reduce waste, and improve sustainability performance. Case studies of companies like Procter & Gamble, Microsoft, and Unilever demonstrate successful strategies and outcomes achieved through Sustainable BI. Best practices for Sustainable BI adoption include leadership commitment, cross-functional collaboration, and ethical considerations. Challenges include data privacy, scalability, and the need for continuous improvement and adaptability. Emerging technologies such as AI, IoT, and blockchain are shaping the future of Sustainable BI, offering new capabilities for sustainability performance. In conclusion, Sustainable BI represents a holistic approach to business intelligence that aligns with sustainability principles and offers opportunities for long-term growth and positive societal and environmental impact.