The need for solid security in web applications could not be more critical, considering they are becoming increasingly integral to business operations and personal interaction. Traditional Web vulnerability scanners are typically encumbered by the intricacy and dynamics of the current threats. Deep learning, being part of artificial intelligence, has powered this potential toward magnifying the detection of vulnerabilities through its analytic capabilities, which are precise, adaptive, and in real time. This paper investigates the role of deep learning in web vulnerability scanning for a view of attaining a considerable reduction in false positive/negative cases, zero-day vulnerability detection, and scalability in accordance with the very complex needs of the web environment. A small fraction of surveyed applications of the deep learning-based scanners includes, among others, DeepVulSeeker, AI-Sec, and DeepDefender. All of these specify the practical benefits that can be derived from the kind of technology at hand in real-world scenarios. Case studies provided improved detection rates and a reduction in the successful cases of cyber-attacks; hence, this signifies the ability of deep learning to totally revolutionize web security. Of course, deep learning applied to this field is not devoid of challenges either: large computational resources, huge amounts of very diverse training datasets, risk of overfitting—and all of that topped by issues of model interpretability. This paper concludes that it is in further acts of innovation and fine-tuning deep learning techniques that the future in web vulnerability scanning will lie toward the eventual development of a much more secure and resilient digital environment.