A Multi-Domain Predictive Analytics Framework for Intelligent Forecasting using AI-Driven Models

Predictive analytics plays a crucial role in modern data-driven decision-making across domains such as real estate, customer relationship management, transportation, and environmental monitoring. Traditional predictive systems typically require task-specific machine learning models, large labeled datasets, and extensive preprocessing pipelines, which can limit scalability and adaptability across multiple domains. This research presents a unified predictive analytics framework capable of performing diverse forecasting and classification tasks through an intelligent AI-driven prediction engine integrated within a web-based dashboard. The proposed system supports multiple predictive applications including house price estimation, customer churn prediction, CO? emission estimation, and fuel consumption forecasting. A modular architecture is implemented using a lightweight web framework to provide an interactive and scalable interface for users. The proposed framework demonstrates that multi-domain predictive analytics can be achieved through a unified platform without requiring separate model pipelines for each task. Experimental evaluation shows that the system provides reliable predictions while simplifying traditional machine learning workflows. The platform offers scalability, ease of integration, and real-time prediction capabilities, making it suitable for intelligent decision-support systems across various industries.