With the global population steadily increasing, there is a heightened demand for food production, necessitating innovative approaches to overcome challenges such as decreasing cultivable land and stagnant crop yields. Plant diseases, particularly those caused by air-borne fungi, pose significant threats to agricultural productivity, accounting for substantial crop losses. Climate change exacerbates these challenges by altering the distribution and behavior of phytopathogenic fungi. To address these issues, advanced biotechnological tools, such as genomic prediction models, are being employed in research related to plants. This chapter reviews the application of genomic prediction models in mitigating air-borne plant diseases, focusing on methodologies, model development approaches, and the integration of machine learning techniques. Furthermore, the scope of machine learning in genomic prediction is discussed, highlighting its potential in disease prediction, population genetics studies, functional genomics, biological feature prediction, etc. Overall, genomic prediction models offer promising avenues for enhancing crop resilience and optimizing disease management strategies in agriculture.