A Critical Review of Interpretable and Robust Machine Learning for HighImpact Use Cases

The rapid integration of machine learning into critical sectors such as healthcare, finance, transportation, and cybersecurity has intensified the need for models that are not only accurate but also transparent and dependable. In high impact environments, unreliable predictions or opaque decision processes can lead to significant operational, ethical, and economic consequences. This systematic review investigates contemporary approaches that enhance interpretability and robustness in machine learning systems. The study examines various model architectures, training strategies, robustness improvement techniques, and explanation frameworks developed to support trustworthy deployment. It reviews evaluation methodologies used to assess stability under noisy data, adversarial conditions, and distributional shifts, while also analyzing metrics designed to measure explanation quality and uncertainty. The findings reveal a growing transition toward hybrid learning frameworks that combine predictive strength with structured transparency. Persistent challenges include balancing model complexity with explainability , ensuring scalability in large scale systems, and establishing standardized benchmarks for robustness evaluation. The review concludes by outlining future research directions focused on intrinsic interpretability, adaptive learning under dynamic environments, and integrated evaluation protocols that promote reliable machine learning in high impact applications.