Machine Learning for Predictive Modeling Across Critical Domains: A Comprehensive Review of Algorithms, Applications, and Future Directions

N. M. Alkudah*

doi:10.5281/zenodo.21212309

International Journal of AI and Advanced Computing

Volume 1 , Issue 2

Review Article • Open Access

Machine Learning for Predictive Modeling Across Critical Domains: A Comprehensive Review of Algorithms, Applications, and Future Directions

N. M. Alkudah*

Pages 60-74

DOI: 10.5281/zenodo.21212309

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Abstract

The proliferation of machine learning algorithms across diverse scientific and industrial domains has revolutionized predictive modeling capabilities, enabling data-driven decision-making in complex systems where traditional analytical approaches prove insufficient. This comprehensive review examines the application of machine learning algorithms for predictive modeling across seven critical domains: cybersecurity threat detection, photovoltaic system fault diagnosis, pharmaceutical drug delivery systems, environmental rainfall forecasting, reinforcement learning for autonomous navigation, reinforcement learning for adaptive cybersecurity defense, and causal reinforcement learning for decision-making under uncertainty. Through systematic synthesis of recent empirical studies and comparative analyses, this paper investigates the performance of various supervised, unsupervised, and reinforcement learning algorithms including Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbors, Artificial Neural Networks, ensemble methods, Proximal Policy Optimization, Soft Actor-Critic, and Deep Q-Networks. The review reveals that ensemble-based learning approaches consistently outperform single-model architectures across supervised learning domains, with Random Forest achieving F1-scores of 97.5% in cybersecurity threat detection and 98.88% accuracy in photovoltaic fault diagnosis. Deep learning architectures, particularly Convolutional Neural Networks and Long Short-Term Memory networks, demonstrate superior capability in capturing complex spatiotemporal patterns. In reinforcement learning applications, Proximal Policy Optimization with adaptive curriculum learning achieves navigation success rates of 92% in complex dynamic environments, while reinforcement learning-based CAPTCHA defense systems achieve 97.7% accuracy in distinguishing human users from automated bots. Causal reinforcement learning frameworks demonstrate that incorporating structural causal knowledge can improve sample efficiency by 35-40% compared to model-free approaches. The review identifies key challenges including data quality issues, model interpretability limitations, computational scalability constraints, generalization gaps across domains, and the need for standardized benchmarking frameworks. Future research directions include the development of explainable AI methods, federated learning architectures for privacy-preserving distributed modeling, hybrid physics-informed machine learning approaches, domain-specific optimization strategies, and the integration of causal reasoning with reinforcement learning for robust decision-making. The findings provide a comprehensive framework for algorithm selection across domains, emphasizing that while deep learning excels in complex pattern recognition tasks, ensemble methods offer robust performance with greater interpretability, and reinforcement learning provides a unifying paradigm for sequential decision-making under uncertainty.

Keywords

Machine Learning, Predictive Modeling, Cybersecurity Threat Detection, Photovoltaic Fault Diagnosis, Pharmaceutical Drug Delivery, Rainfall Forecasting, Reinforcement Learning, Autonomous Navigation

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