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The fast rate of adoption of cloud-integrated Internet of Things (IoT) environs poses critical problems to access control because of the resource restrictiveness, heterogeneity, and dynamism of IoT devices, making the traditional centralized security models not applicable. To overcome these shortcomings, this paper presents a blockchain-based distributed access control system CapBlock, which combines the ability-based access control (CapBAC), smart contracts, and machine learning (ML) to provide dynamic, scalable, and resistant security enforcement. The framework applies the Ensemble Cuckoo Search Optimization (ECSO) algorithm, Bloom filters and the public-key cryptography to optimize the usage of resources, reduce the latency associated with authorization and improve the scalability of the system, and the use of a Random Forest-based anomaly detection model is continuously used to monitor the behavior of users to access the system and dynamically update the access policies to prevent malicious activities. The system is tested on a large scale of simulation with 4,000 access transactions on metrics such as accuracy, precision, recall, F1-score, transaction latency, throughput, security of data and preservation of privacy. The user privacy protection rate is considered as the percentage of requests of access where sensitive user attributes are not disclosed during authentication, authorization, and blockchain transaction logging, where the average value is 93 percent. Moreover, the throughput is calculated by scaling loads of request-per-second in HTTP requests to blockchain transactions commits with the assumption of one validated request per one completed transaction, under which the proposed framework can reach the highest throughput of 1,200 transactions per second (TPS) with stable performance. The experimental findings support the notion that CapBlock is a highly secure system capable of promoting security resiliency, privacy protection, and scalable access control in the contemporary cloud-IoT systems.