Artificial Intelligence for Predictive Analytics and Intelligent Decision Systems Across Domains

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology for predictive analytics and intelligent decision-making across diverse application domains. This study presents a unified multi-domain AI framework that integrates advanced deep learning models to address complex prediction tasks in healthcare, financial systems, intelligent transportation, and recommendation platforms. The proposed architecture combines domain-specific modeling techniques, including sequential learning for time-series prediction, hybrid feature interaction models for financial forecasting, real-time object detection for transportation systems, and sequence-aware recommendation mechanisms for personalized user experiences. The framework is designed to handle heterogeneous datasets through a structured pipeline consisting of data acquisition, preprocessing, model training, and a unified decision layer. Experimental analysis demonstrates that the proposed system achieves high predictive accuracy, robustness, and scalability across all domains. The integration of multiple AI models within a single architecture enables efficient cross-domain knowledge utilization and supports real-time decision-making in dynamic environments. Furthermore, the study highlights key challenges, including computational complexity, data dependency, and the need for model interpretability. Future directions focus on enhancing explainability, optimizing model efficiency, and enabling secure and scalable deployment in real-world applications. The proposed framework contributes to the advancement of next-generation AI systems and provides a comprehensive foundation for multi-domain predictive analytics research.