In today's fast-paced corporate environment, efficient management of customer inquiries is critical for maintaining high satisfaction and productivity levels. This study explores developing and implementing a web-based ticketing system powered by artificial intelligence (AI) and natural language processing (NLP) to address this need. Designed to streamline support operations, the system enables users to submit problems through an intuitive online form, where inquiries are categorized and prioritized based on predefined properties such as urgency, complexity, and type. This research conducts a systematic review to evaluate the role of AI and NLP technologies in automating ticket classification, prioritization, and routing. The methodology includes rigorous data collection, quality assessment, and exploration of existing literature on AI-driven ticketing systems and their effectiveness in optimizing customer support workflows. Findings highlight the transformative potential of AI in reducing response times, improving resource allocation, and enhancing the overall efficiency of support operations. The study emphasizes the significant role of intelligent ticketing tools in creating scalable, user-centric solutions that adapt to the growing demands of organizations across industries. By integrating automated processing with manual oversight for complex cases, this system demonstrates the ability to address diverse customer needs while maintaining operational flexibility. This research provides a foundational analysis for future advancements in AI-powered support systems, contributing to the broader field of enterprise resource management and intelligent automation.