This study investigates the role of digitalization and advanced computing technologies in enhancing sustainability across key sectors, including agriculture, water management, energy systems, climate research, and manufacturing. The objective is to assess how innovations like federated learning, blockchain, edge computing, digital twins, and quantum computing, with traditional methods such as artificial intelligence (AI), remote sensing, and precision farming, contribute to the achievement of the United Nations’ Sustainable Development Goals. The study highlights the advantages of these technologies, such as enhanced efficiency, resource optimization, and data-driven decision-making. However, it also identifies challenges, including high implementation costs, data dependency, and gaps in digital literacy, which may hinder their widespread adoption. Additionally, the research presents recommendations for improving low-cost biodegradable sensors, explainable AI models, and hybrid energy systems to address these limitations. The findings emphasize the need for inclusive infrastructure development, effective policymaking, and collaborative efforts to maximize the potential impact of these technologies on sustainability. Overall, the study provides a comprehensive overview of the current landscape and suggests avenues for further progress in utilizing computing technologies to support sustainable development.

Digitalization Federated Learning Artificial Intelligence (AI) Global Challenges Efficiency and Optimization