Sentiment analysis has emerged as an essential component of Natural Language Processing (NLP) for comprehending views in fields such as social media, economics, and healthcare. Nonetheless, deep learning models, despite their great accuracy, often exhibit a deficiency in interpretability, limiting their practical use in critical decision-making contexts. This paper introduces a Hybrid Transformer-ML model that combines BERT-based feature extraction with conventional machine learning classifiers (Random Forest, SVM, and Logistic Regression) to improve accuracy and explainability. The suggested approach guarantees transparency in sentiment categorization by using SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Approach-Agnostic Explanations). Experimental findings across several benchmark datasets indicate that the Hybrid Transformer-ML model surpasses independent deep learning models, with a 95.3% accuracy rate while preserving good interpretability. The results indicate that this method is appropriate for applications necessitating both accuracy and clarity, including financial sentiment analysis and healthcare surveillance.