This paper presents a blockchain-enabled eXplainable AI (XAI) framework for enhancing decision-making in cyber threat detection within Smart Healthcare Systems (SHS). The framework addresses three key research questions: (1) ensuring data integrity and authenticity for cyber threat detection modeling, (2) improving the performance of cyber threat detection during testing using healthcare datasets, and (3) providing transparency to comprehend and interpret the decisions of the cyber threat detection model. To address RQ1, a blockchain-enabled peer-to-peer cloud server is designed using a Clique Proof-of-Authority (C-PoA) consensus mechanism to ensure data integrity between multiple cloud vendors. Attribute-based authentication is implemented to prevent insider attacks. To address RQ2, a novel deep learning-based threat detection model is developed by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi-head attention mechanism for improved attack detection in SHS. The model is evaluated using two publicly available datasets, ToN-IoT and IoT Healthcare Security Dataset, and metrics such as accuracy, precision, recall, F1 score, confusion matrix, and ROC curve are used to assess its effectiveness. To address RQ3, a model-agnostic XAI technique based on the SHapley Additive exPlanations (SHAP) mechanism is used for local and global interpretation of data features and cyber threat detection output to improve the decision-making capability of cybersecurity experts. The framework includes a blockchain-enabled cloud server, an enhanced decision support system, and a deep learning-based intrusion detection system. The blockchain mechanism ensures secure data sharing and authentication between entities, while the deep learning model uses PSLSTM and multi-head attention to improve attack detection. The SHAP mechanism provides transparency and interpretability for the model's decisions. The experimental evaluation shows that the proposed framework outperforms existing methods in terms of accuracy, precision, recall, and F1 score. The results demonstrate the effectiveness of the blockchain-enabled XAI framework in enhancing decision-making in cyber threat detection within SHS.This paper presents a blockchain-enabled eXplainable AI (XAI) framework for enhancing decision-making in cyber threat detection within Smart Healthcare Systems (SHS). The framework addresses three key research questions: (1) ensuring data integrity and authenticity for cyber threat detection modeling, (2) improving the performance of cyber threat detection during testing using healthcare datasets, and (3) providing transparency to comprehend and interpret the decisions of the cyber threat detection model. To address RQ1, a blockchain-enabled peer-to-peer cloud server is designed using a Clique Proof-of-Authority (C-PoA) consensus mechanism to ensure data integrity between multiple cloud vendors. Attribute-based authentication is implemented to prevent insider attacks. To address RQ2, a novel deep learning-based threat detection model is developed by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi-head attention mechanism for improved attack detection in SHS. The model is evaluated using two publicly available datasets, ToN-IoT and IoT Healthcare Security Dataset, and metrics such as accuracy, precision, recall, F1 score, confusion matrix, and ROC curve are used to assess its effectiveness. To address RQ3, a model-agnostic XAI technique based on the SHapley Additive exPlanations (SHAP) mechanism is used for local and global interpretation of data features and cyber threat detection output to improve the decision-making capability of cybersecurity experts. The framework includes a blockchain-enabled cloud server, an enhanced decision support system, and a deep learning-based intrusion detection system. The blockchain mechanism ensures secure data sharing and authentication between entities, while the deep learning model uses PSLSTM and multi-head attention to improve attack detection. The SHAP mechanism provides transparency and interpretability for the model's decisions. The experimental evaluation shows that the proposed framework outperforms existing methods in terms of accuracy, precision, recall, and F1 score. The results demonstrate the effectiveness of the blockchain-enabled XAI framework in enhancing decision-making in cyber threat detection within SHS.