The hospitality industry increasingly relies on data-driven decision-making, yet existing hotel management systems often employ opaque, rule-based algorithms that fail to adapt to dynamic customer behaviors and market conditions. We propose an Explainable AI Framework for Intelligent Hotel Management that integrates three core components: a hybrid recommendation engine, a dynamic pricing model, and an NLP-driven sentiment analysis module. The recommendation system combines collaborative filtering with content-based analysis through a Transformer-encoder architecture, generating personalized room suggestions that evolve with user interactions. A Gradient Boosting Decision Tree enhanced with temporal features optimizes room rates in real time by balancing revenue maximization against occupancy probability, thereby replacing static tariff structures. The sentiment analysis module fine-tunes a pre-trained BERT model on hospitality-specific data to extract sentiment polarity and complaint severity, automatically escalating high-severity issues to senior management. Crucially, the framework incorporates Shapley Additive exPlanations to render each algorithmic output interpretable; feature contribution values are computed for every recommendation, price decision, and classification result. These explanations are injected into managerial dashboards and user interfaces, transforming complex model inferences into transparent narratives. The proposed system therefore bridges the gap between advanced machine learning and human-centric operational oversight. Its novelty lies in the seamless integration of explainability into a multi-modal pipeline that addresses personalization, pricing, and customer engagement simultaneously. This work demonstrates that high-performance AI can be both powerful and interpretable, offering a practical pathway for hotels to enhance operational efficiency while maintaining stakeholder trust.