Planning an event is a complicated process that includes budgeting, vendor selection, and logistical management. Classical event planning approaches, which depend upon manual processes and static budget estimates, frequently result in inconsistencies, miscalculations, and expense overruns. Artificial intelligence (AI) is now recognized as a game-changing technology for event planning, work automated processes, maximizing resource utilization, and boosting cost estimation accuracy. The paper looks at the use of computational intelligence in event planning and cost prediction, with a focus on key approaches, datasets, and technologies that might help improve efficiency. It studies the application of machine learning techniques, natural language processing (NLP), and predictive analytics to make intelligent suggestions for cost estimation, vendor selection, and real-time budget changes. The integration of AI-driven dynamic pricing models and automated contract negotiation systems is also investigated to determine their influence on lowering financial risks and assuring budget optimization. This study also covers the problems and moral consequences that come with AI-driven event planning, such as data privacy concerns, algorithmic prejudice, and market reliance. The findings show that AI-powered event management systems improve decision-making, minimize human mistakes, and save money by providing real-time, data-driven insights. Future advances in AI and deep learning are likely to enhance event planning automation, making it simpler, flexible, and cost-effective.
AI, Machine Learning, Event Planning, Cost Estimation, Budget Optimization.
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