Legume crops, particularly chickpeas, are highly nutritious and play a vital role in global food security. However, they are susceptible to various diseases, among which Fusarium wilt, caused by Fusarium oxysporum, leads to significant yield losses. Early detection of Fusarium wilt is essential for effective disease management. Traditional diagnostic methods are often labour-intensive and time-consuming. This study aims to classify Fusarium wilt in chickpeas using Deep Convolutional Neural Networks (DCNN). The dataset consists of 4,339 chickpea plant images obtained from Kaggle. The images are categorized into five classes based on disease severity: highly resistant (HR), resistant (R), moderately resistant (MR), susceptible (S), and highly susceptible (HS). The images were pre-processed, resized, normalized, and augmented to enhance model performance. The classification was performed using a SoftMax classifier. The DCNN was trained using the Adam optimizer and categorical cross-entropy as the loss function, with hyperparameters fine-tuned to optimize performance. The proposed model achieved an overall accuracy of 73.96%, with a training accuracy of 73.16% and a validation accuracy of 77.64% after 100 epochs. Performance metrics revealed the highest precision and recall for the highly susceptible (HS) class, while accuracy was lower for intermediate classes (R and MR). The confusion matrix highlighted areas where the model excelled and where further refinement is needed. The study demonstrates the potential of DCNNs for automated classification of Fusarium wilt in chickpeas, offering a practical tool for disease management. However, the model's limitations in intermediate classes underline the need for further improvements. Future work will focus on enhancing dataset diversity, refining preprocessing techniques, and exploring advanced architectures to improve classification accuracy across all severity levels. These findings contribute to the development of robust, automated solutions for managing plant diseases and supporting sustainable agriculture.