An Intelligent and Explainable Machine Learning Framework for SMS Spam Detection

Short Message Service (SMS) spam has become a significant threat to mobile users, leading to privacy breaches, financial fraud, and poor user experience.Traditional spam detection techniques rely heavily on black-box machine learning models that lack transparency and interpretability.This paper proposes an intelligent and explainable machine learning framework for SMS spam detection that not only achieves high classification accuracy but also provides clear explanations for model decisions.The framework integrates classical machine learning algorithms with explainability techniques to identify influential features contributing to spam classification.Experimental results demonstrate that the proposed framework improves trustworthiness and interpretability while maintaining competitive detection performance.