Machine Learning and Statistical Learning with Applications 2025

This Special Issue, "Machine Learning and Statistical Learning with Applications 2025", presents a curated collection of recent advances at the intersection of methodological innovation and real-world impact. This Special Issue brings together 14 contributions spanning diverse domains, including healthcare, environmental systems, finance, and socio-technical applications. The articles highlight how modern machine learning approaches, from deep learning and hybrid models to statistical and interpretable methods, are being adapted to address complex, data-intensive problems. A key theme across the collection is the growing emphasis on reliability and applicability. Beyond predictive accuracy, the featured works explore challenges such as interpretability, uncertainty estimation, data scarcity, and deployment in high-stakes environments. Several studies demonstrate the effectiveness of combining traditional statistical techniques with contemporary machine learning models, reflecting a broader trend toward integrated and application-aware solutions. Overall, this Special Issue highlights the evolving role of machine learning and statistical learning as essential tools for data-driven discovery and decision making, while emphasizing the importance of robustness, transparency, and practical relevance in modern research.