A Review of Explainable and Reliable Machine Learning Models for Real-World Applications

Machine learning has become a foundational tool for data driven decision making across sectors such as healthcare finance manufacturing and smart infrastructure. However, models developed under controlled experimental settings often fail to maintain performance and reliability when exposed to noisy imbalanced and dynamically changing real world data. In addition, many high performing algorithms lack transparency which limits their adoption in sensitive and regulated domains. This review examines existing approaches aimed at improving both interpretability and robustness in machine learning systems. It analyzes model architectures data preprocessing strategies robustness enhancement techniques and interpretability frameworks used to explain predictions and assess uncertainty. Comparative evaluation practices and validation standards are discussed to understand how stability and transparency are measured in practical applications. The review identifies persistent challenges including tradeoffs between accuracy and explainability vulnerability to data shifts and scalability constraints in large scale environments. Finally emerging research directions are outlined with emphasis on integrated frameworks that combine robust learning strategies with interpretable model design for trustworthy deployment in real world settings.