The deep learning models are being used in the agricultural sector, ushering in a new age of decision support for crop pest and disease control. In light of the difficulties faced by farmers, this literature study seeks to identify the critical components of deep learning models that affect decision support. All the way from model design to data input to training approaches and their effects on efficient decision-making. Examining the deep learning model factors influencing decision support in crop diseases and pest management was the primary goal. The researcher looked at articles and journals published by IEEE access, ACM, Springer, Google Scholar, Wiley online library, Taylor and Francis, and Springer from 2014 to 2024. From the search results, sixty-three publications were selected according to their titles. The paper provides a synopsis of deep learning models used for crop health management based on a careful evaluation of scholarly literature. In order to shed light on the merits and shortcomings of different models, the article conducts a thorough literature review and literature synthesis. Future studies might be guided by the identification of methodological issues and gaps in present research. Applying deep learning to the problem of agricultural diseases and pest control has real-world consequences, as shown by several case studies and applications. Insightful for academics, practitioners, and legislators in the field of precision agriculture, this extensive study adds to our knowledge of the complex relationship between elements in deep learning models and their impact on decision support.