<p>The improvement of economic, social, and environmental health is dependent on sustainable, renewable, and clean energy, which promotes productivity and economic growth. In order to lessen ecological harm and stop global warming, the Sustainable Development Goals (SDG) is useful to turn fossil fuels into renewable energy sources. Citizens must be made aware of clean energy concepts and technologies from their schools and colleges to pursue professions in clean energy. However, there are drawbacks, such as environmental optimization, inadequate implementation, and curriculum design. Innovation and entrepreneurship education can foster the development of individual talents by realizing their potential and help to improve the economic and social sectors by offering innovative ideas for the nation. Even though education on sustainability and renewable energy is important, degree holders are still unsure about these subjects. Assessing the topics' applicability is key in encouraging a more sustainability-focused approach to developing educational strategies. Thus, a new approach for career development and innovation entrepreneurship research for students in clean energy is proposed. At first, data needed for this research work is collected. Then, the collected data are initialized with weights using Rank Order (RO) and entropy weight method. The weighted data are sent to the adaptive dilated Recurrent Neural Networks-based decision support system. The Spider Monkey Optimization (SMO) is utilized to progress the capability of the presented network. The proposed models help increase the sustainability of innovative education for clean energy majors. The efficiency of the recommended model is justified by comparing the simulation results with other algorithms and deep learning mechanisms.</p>