ntelligent Event Planning: A Framework for ML-Enhanced Budgeting and Vendor Selection on Mobile Platforms

This article examines the integration of machine learning techniques in Android-based party planning applications to address the dual challenges of budget optimization and vendor selection. The article presents a framework that processes user inputs, including budget constraints, guest count, and style preferences, to generate personalized recommendations for caterers, decorators, and venues. Using TensorFlow Lite, the proposed system employs predictive analytics to anticipate expenses and facilitates dynamic budget adjustments based on real-time vendor data. The methodology addresses significant technical challenges, including integrating localized vendor information, handling unstructured user inputs, and maintaining computational efficiency on mobile devices. Experimental results demonstrate improvements in recommendation relevance, budget accuracy, and user satisfaction compared to conventional planning approaches. Beyond technical contributions, the article highlights broader implications for pricing transparency in the event industry, support for local businesses through targeted recommendations, and promoting sustainable event planning practices. This article establishes a foundation for future developments in AI-assisted decision support systems for consumerfacing event planning applications.