Effective management of finite resources in precision agriculture requires efficient technologies to generate reliable data about crops, pastures, soil, water sources, climate, pests, diseases, and other variables. These data enable farmers to make informed decisions to enhance efficiency and make their production more sustainable. This review aimed to assess the technological advances in precision agriculture in terms of their benefits, constraints, and potential for sustainable farming practices. A total of 132 scientific papers were selected, analyzed, and discussed to explore the current status and the future of precision agriculture in relation to sustainable development. This review covers technologies utilized in planting, crop monitoring, resource management, decision support systems, and automation. The application of artificial intelligence (AI)-driven technologies, including machine learning, computer vision, and sensor technologies, transforms traditional farming and contributes to resolving its limitations by providing farmers with real-time data and actionable insights. Ethical considerations, data security, and the digital divide are among the key challenges needing attention. Interdisciplinary collaboration is also needed to tackle complex issues associated with the sustainable implementation of advanced technologies, including AI in precision agriculture. Precision agriculture technologies have a transformative impact on traditional farming. The integration of AI contributes to higher productivity and efficiency, as well as long-term sustainability of farming practices, ensuring food security for the growing population. © © 2025, Diakite et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.