The surge of visual data, particularly images and videos, makes robust wireless visual communication indispensable in the digital age. With the advent of sixth-generation (6G) networks featuring ultra-high data rates and applications like XR and telemedicine the demand for efficient image transmission is clear. Traditional communication systems, relying on independent source and channel coding, fail to perform adequately in dynamic wireless environments. Deep Joint Source Channel Coding (Deep JSCC) emerges as a transformative approach that integrates data compression and error protection at the source-channel level, utilizing advancements in deep learning. Advanced architectures, such as autoencoders and adaptive schemes, provide enhanced perceptual quality and adaptability across varying channel conditions. This review highlights significant progress in Deep JSCC for wireless image transmission in 6G networks, focusing on theoretical foundations and applications in IoT, immersive XR, telemedicine, and vehicular communications. It also addresses challenges like computational complexity and standardization, while identifying future opportunities in semantic communications and green AI............... Keywords:..................(Deep JSCC), 6G Wireless Networks, Robust Image transmission, Semantic Communications, (IoT), (XR), Machine Learning for Communications.