Engineering of the Future: Combining Big Data, Analytics and Artificial Intelligence to Develop Sustainable Technological Solutions

Purpose: This paper aims to critically review the integration of big data analytics and AI in engineering systems for addressing climate change, energy efficiency and resource optimisation, and to identify the key barriers to their wider adoption. Design/Methodology/Approach: The study adopts a qualitative research methodology based on secondary data analysis of 17 peer-reviewed articles published between 2016 and 2026, using thematic analysis to identify key trends and patterns. Research Limitation: Challenges associated with implementing the described systems include high costs and data security concerns, indicating the need for further research on secure and cost-effective implementation. Findings: Big Data, Analytics, and Artificial Intelligence (AI) can enhance engineering decision-making by improving sustainability and efficiency. Data-driven methods are used to monitor and optimise industrial processes, with lower energy usage and waste. Predictive analysis reduces downtime and equipment failures using machine learning. Practical Implication: The combined use of digital technologies supports resource optimisation, environmental monitoring through carbon footprint tracking and climate modelling, and the transformation of traditional reactive engineering systems into proactive and autonomous systems. Social Implication: AI- and Big Data-related solutions improve smart cities, transportation systems, and buildings through traffic flow reduction, lower emissions, and more sustainable urban planning. Originality/Value: Unlike application-specific studies, this article contributes to knowledge by synthesising big data, analytics, and AI applications in engineering into five cross-cutting themes and outlining conditions for their sustainability effect, such as integration with cyber-physical systems.