This review paper explores the intersection of plant disease, climate change, and deep learning techniques. The changing climate significantly impacts plant health, leading to the emergence and spread of various diseases that pose significant challenges to agricultural productivity and food security. Deep learning, a subfield of machine learning, has shown great promise in addressing these challenges by providing powerful tools for disease detection, diagnosis, and prediction. In this review, we examine the current state of plant disease management, the impact of climate change on plant health, and the application of deep learning methods in mitigating and understanding plant diseases under changing climatic conditions. We present an overview of the critical deep learning approaches used in plant disease research and highlight their advantages, limitations, and potential future directions. Additionally, we discuss the availability and challenges associated with data collection, preprocessing, and model deployment in the context of plant disease and climate change research. By embracing the synergy between climate change, plant diseases, and deep learning, we aim to provide valuable insights and foster further research in plant disease management.