Big Data and AI Algorithms for Sustainable Development Goals: A Topic Modeling Analysis

This study makes significant contributions to the field by examining the transformative role of big data and artificial intelligence (AI) in advancing Sustainable Development Goals (SDGs), particularly healthcare (SDG3), sustainable energy (SDG7), and industry and infrastructure (SDG9). Using BERTopic modeling, a machine learning technique, this research systematically analyzes literature from 2013 to 2024, providing an overview of AI and big data applications mapped to SDGs which is a first. This structured approach identifies key SDGs impacted by these technologies and highlights interdisciplinary methods that further enhance SDG outcomes. AI applications notably improve healthcare by advancing disease tracking, tailored treatments, and precision medicine, fostering universal healthcare and reducing noncommunicable disease mortality. In energy, AI-driven solutions optimize forecasting, grid management, and renewable integration, while in industry, they bolster infrastructure resilience through innovations like predictive maintenance and automated quality control within Industry 4.0 frameworks. The integration of automated text analysis and semantic context captures broad trends, contributing both methodologically and substantively at the intersection of AI and sustainability. Despite these advancements, the study underscores ethical concerns, including data privacy, security, and algorithmic biases. Interdisciplinary collaboration among healthcare professionals, engineers, environmental scientists, and AI experts is crucial to developing ethical, scalable AI solutions. The study suggests future research focus on AI transparency, scaling across diverse sectors, and integrating advanced techniques such as neurosymbolic AI and quantum neural networks to enhance system reliability. These insights offer practical implications, reinforcing the potential of AI and big data to address global challenges sustainably while calling for balanced attention to ethical and regulatory dimensions.