Convolutional Neural Networks (CNNs) are a cornerstone research area in deep learning, focusing on architectures designed to automatically learn spatial hierarchies of features from structured data such as images, videos, and spatial-temporal sensor data. Research papers in this domain explore CNN variants including AlexNet, VGGNet, ResNet, DenseNet, Inception, and more recent hybrid architectures that combine CNNs with recurrent networks or attention mechanisms. Key applications include image classification, object detection, semantic segmentation, facial recognition, medical image analysis, video understanding, and remote sensing. Contributions also cover techniques for improving training efficiency, handling large-scale datasets, transfer learning, data augmentation, and robust feature extraction. Recent studies address challenges such as adversarial attacks, interpretability of learned features, real-time inference on edge devices, and multi-modal data integration. By leveraging CNNs, research in this area aims to provide accurate, scalable, and adaptive solutions for complex visual and spatial data analysis tasks.