Integrating Artificial Intelligence Techniques into Machine Learning Models: Enhancing Predictive Accuracy, Adaptability, and Decision Making in Complex Data Environments

Abstract

Content of the Abstract

Artificial Intelligence (AI) integration into machine learning (ML) has accelerated advancements in predictive analytics, adaptive learning, and strategic decision-making across complex data environments. This paper explores the synergy between AI techniques and traditional ML models, emphasizing improvements in accuracy, adaptability, and real-world applicability.

Purpose
The purpose of this study is to investigate how AI methods enhance standard machine learning frameworks, addressing challenges in dynamic data processing, model adaptability, and decision support.

Design/methodology/approach
This research employs a mixed-method approach combining theoretical analysis, comparative evaluation of hybrid AI-ML architectures, and empirical assessment on benchmark datasets.

Findings
The study finds that integrating AI techniques—such as deep learning optimization, reinforcement learning components, and adaptive ensemble strategies—significantly improves model performance metrics, reducing error rates while enhancing adaptability in complex environments.

Practical implications

Results offer insight for practitioners seeking to deploy AI-enhanced ML systems in domains like healthcare diagnostics, financial forecasting, and autonomous systems, suggesting practical frameworks and performance benchmarks.

Originality/value
This paper synthesizes cross-disciplinary advances in AI and ML before 2015 and connects them with modern integrative techniques, presenting novel insights into hybrid model design and evaluation.