This abstract presents a comprehensive review of ten studies examining the intersection of deep learning and multi-camera systems. The analysis focuses on the adoption and the challenges of generative adversarial network (GANs), particularly in image segmentation and texture synthesis. It also explores 3D reconstruction methodologies for indoor environments and dynamic scenes, introducing innovative systems like R3D3 for autonomous driving challenges. The study further proposes a method for multi-view depth estimation and introduces an online multi-view depth prediction strategy for superior performance in indoor environments. Additionally, it critically assesses advancements in multi-camera tracking (MCT) algorithms and surveys the literature on physical arrangements, calibrations, and applications of multi-camera systems in various fields like surveillance, sports, education, and smartphone technology. The abstract emphasizes the holistic understanding provided by this review for researchers, practitioners, and decision-makers in diverse domains, without altering the original meaning.