The rapid growth of Internet of Things (IoT) devices, distributed applications, and cloud native services has significantly increased the demand for scalable, intelligent, and transparent data processing frameworks. Conventional cloud computing architectures often face challenges related to latency, resource utilization, interpretability of artificial intelligence (AI) models, and dynamic workload management. To address these limitations, this study proposes a Hybrid Explainable Artificial Intelligence (XAI) framework for scalable and intelligent data processing in cloud-based distributed computing environments. The proposed framework integrates cloud computing, distributed resource management, machine learning, deep learning, and explainable AI techniques to improve computational efficiency while ensuring transparency and trust in automated decision-making. The proposed architecture comprises three functional layers: the Data Acquisition Layer, responsible for collecting and preprocessing heterogeneous data from distributed sources; the Intelligent Processing Layer, which employs hybrid AI models for feature extraction, prediction, resource optimization, and workload scheduling; and the Application and Decision Support Layer, which provides explainable insights, visualization, and intelligent services to end users. Explainable AI mechanisms are incorporated to interpret model predictions, enhance user trust, and improve decision transparency in mission-critical applications. Furthermore, the framework leverages cloud platforms to dynamically allocate computational resources, optimize workload distribution, and support elastic scalability across distributed infrastructures. Experimental analysis demonstrates that the proposed framework improves throughput, reduces processing latency, enhances resource utilization, and lowers operational costs compared with conventional cloud-based processing approaches. The integration of Hybrid AI with Explainable AI enables accurate predictive analytics while providing interpretable outcomes that support reliable decision-making. Consequently, the proposed framework offers an efficient, scalable, and trustworthy solution for next-generation cloud computing applications in smart healthcare, industrial automation, smart cities, and large-scale data analytics.

Keywords: Hybrid Explainable Artificial Intelligence (XAI), Cloud Computing, Distributed Computing, Intelligent Data Processing, Machine Learning, Deep Learning, Resource Optimization, Scalable Computing, Decision Support.