Sustainable business strategy increasingly demands the integration of advanced computational tools that can process complex, multi-dimensional data for informed decision-making. This study presents a comprehensive framework that integrates Artificial Intelligence (AI), Data Analytics, and Decision Modelling through a Multi-Criteria Engineering Approach to enhance sustainability-oriented corporate planning. The framework leverages machine learning algorithms to predict business performance indicators, applies data analytics for pattern discovery, and utilizes Multi-Criteria Decision-Making (MCDM) models such as Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for strategic optimization. Quantitative data from energy-intensive industries were analyzed using AI-driven predictive models to evaluate key sustainability dimensions economic efficiency, social responsibility, and environmental impact. The integrated model provided a systematic ranking of alternative strategies, balancing profitability with environmental compliance and stakeholder value. Findings demonstrate that combining AI and decision modelling enhances strategic agility, reduces uncertainty, and supports transparent sustainability decisions. The proposed framework establishes a scalable blueprint for corporate managers and policymakers to align business growth with sustainable development objectives using data-driven engineering intelligence.
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