Chili plants are prone to a range of illnesses that can have a substantial influence on their overall development and yield. The timely and efficient implementation of management strategies relies heavily on the early detection and correct identification of these disorders. In recent years, there has been a growing body of research that demonstrates the efficacy of deep learning approaches, specifically convolutional neural networks (CNNs), in the field of plant disease identification. The present study introduces a ResNet-CNN classifier as a means of detecting illnesses in chili plants. The ResNet architecture is a convolutional neural network (CNN) model that has exhibited outstanding results in challenges related to the classification of images. The ResNet-CNN classifier effectively utilizes its deep layers and bypasses connections to learn and retrieve high-level features from photos of chili plants. The classifier under consideration has been trained using an extensive dataset consisting of labeled photos that cover a range of diseases affecting chili plants. The photographs have undergone pre-processing techniques in order to improve their quality and standardize their characteristics. The ResNet-CNN algorithm is subsequently developed based on the labeled dataset, adopting various strategies including data augmentation to enhance generalization and alleviate the issue of over fitting. In order to assess the efficacy of the classification algorithm, a distinct test dataset consisting of photos of chili plants afflicted by recognized diseases is employed. The ResNet-CNN classifier exhibits a notable level of efficiency and effectively proves its capability to precisely classify several diseases that impact chili plants. The model that has been trained can serve as a valuable tool for automatic detection and diagnosis of diseases, hence enabling prompt intervention and mitigating losses in crop output.