<![CDATA[Artificial Intelligence (AI) has achieved remarkable success in predictive modeling, yet the lack of explainability in complex models remains a major challenge for adoption in high-stakes domains. This study addresses this problem by developing a machine learning pipeline that integrates explainability techniques with high-performance predictive models. The objectives are to enhance model transparency, evaluate performance on real-world datasets, and compare the proposed approach with conventional baseline models. Experimental evaluation was conducted on healthcare and finance datasets, using gradient boosting models combined with SHAP explanations to provide feature-level interpretability. The results demonstrate that the proposed approach achieves 92.5% accuracy, 91.2% precision, and 90.8% recall, outperforming baseline models while maintaining transparent decision-making. Visualization of feature contributions confirmed that the model’s predictions align with domain knowledge, enhancing trust and accountability. The study highlights the feasibility of balancing predictive performance with explainability, providing a practical framework for deploying AI in critical applications. Limitations include increased computational requirements for large-scale datasets. The findings offer implications for both researchers and practitioners by demonstrating that highly accurate models can remain interpretable, promoting ethical and responsible AI deployment. Future work should explore scalability, real-time interpretability, and application to additional domains, further bridging the gap between predictive power and model transparency.]]>
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