This study examines the application of computer intelligence techniques to optimize sustainable development strategies, focusing on modeling the transition to renewable energy in developing countries. The paper begins with the design and implementation of basic computational models and frameworks that can be used to address the complex challenges associated with sustainable development. The analysis then delves into identifying and developing key performance indicators for sustainable development, and provides a comprehensive framework for measuring progress towards overall social, environmental and economic goals around. The study also explores the development of a robust metric for evaluating the transition to renewable energy, including factors such as energy penetration, renewable energy capacity and greenhouse gas emission costs around. An important part of the research is to examine the policy impact of sustainable development and the renewable energy transition. The paper explores how to use computational simulation and optimization models to predict the likely outcomes of policy interventions, enabling policymakers to make informed decisions and optimize their strategies. Furthermore, the study examines the use of computational techniques to model and forecast technologies used in renewable energy solutions in developing countries. It includes an examination of the socio-economic, infrastructure and behavioral factors affecting the spread of clean energy technologies in these contexts. The insights of this study provide valuable insights for policymakers, development agencies and energy professionals, and provide guidance for data-driven, cybernetic design and implementation.