<p>This study examines the achievement of the Sustainable Development Goals (SDGs) under Agenda 2030 in Australia, with particular emphasis on balancing economic growth, environmental sustainability, electricity consumption, and employment generation. A data-driven hybrid modeling framework integrating forecasting techniques, machine learning models, and goal programming is proposed to address the multidimensional trade-offs inherent in sustainable development planning. Hybrid forecasting models are employed to estimate future trends of key indicators, including Gross Domestic Product (GDP), greenhouse gas (GHG) emissions, electricity consumption, and employment, ensuring consistency with observed real-world data patterns. The forecasting results indicate that the hybrid modeling approach estimates Australia’s GDP at 2093.396, reflecting realistic economic growth trends. These forecasted values are subsequently incorporated into a goal programming framework to optimize conflicting policy objectives. The optimized GDP value obtained through goal programming is 2057.419, which closely aligns with the SDGs target, demonstrating the consistency and reliability of the proposed integrated approach. This close alignment highlights the effectiveness of combining hybrid forecasting with goal programming for informed policy decision-making. Overall, the findings confirm that the proposed framework serves as a robust and practical decision-support tool to assist policymakers in designing balanced strategies for achieving Australia’s 2030 SDG targets.</p>