Predictive Analysis and Optimization in Sustainable Agriculture Facing Climate Change with Emerging Technological Approaches

This paper analyses recent advances in predictive models applied to assessing the impact of climate change on sustainable agriculture and reviews optimization techniques, statistical models and emerging technologies. Through a review of 15 studies between 2013 and 2024, predictive techniques were identified to improve yields and manage resources such as water and fertilizers. The results show that most studies on sustainable agriculture focus on predictive models, followed by historical combinations and reviews, reflecting the interest in using data to improve sustainability. It also highlights the use of emerging technologies such as IoT, AI, Big Data and Blockchain, together with optimization techniques and statistical models, to improve efficiency and adaptation in agriculture to climate and production changes. It concludes that these technologies are essential to strengthen food security and agricultural resilience in the face of an uncertain climate environment.