Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3719 2019 2019 Department of Travel and Tourism Management, Faculty of Tourism, King Abdulaziz University, Jeddah 21589, Saudi Arabia admin admin The Internet of Things (IoT) has extensively converted the industry of tourism, reforming travel design, supply, and experiences. The technology of Blockchain (BC) signifies a paradigm shift with the latent to transform many industries, more like spreadsheets altered office efficiency. BC technology provides frequent potential advantages to the tourism industry, with enhanced transparency, security, and efficacy in regions such as payments, bookings, and identity verification, which potentially mains to a more perfect and reliable travel experience. In the tourism region, BC with IoT is mainly attractive owing to the latent benefits it provides in terms of improving the experience of tourism, enhancing operational efficacy, and guaranteeing data security and transactions. Recently, numerous scholars globally have employed deep learning (DL) technology in the industry of tourism to combine physical and social influences for improved travel recommendation services. This study presents a Blockchain for Tourism Service Customization and Management using Whale‐goshawk Optimization Algorithm (BCTSCM-WOA) technique. The main goal of the BCTSCM-WOA method relies on improving the effectual model for tourism service customization. Initially, blockchain technology is applied to provide secure, transparent, and decentralized solutions for handling traveler data, payments, and service personalization. Then, the data pre-processing employs min‐max scaling to transform input data into a suitable format. Besides, the crayfish optimization algorithm (COA) to select the most relevant features from the data has executed the feature selection procedure. For the classification process, the proposed BCTSCM-WOA method projects multi-dimensional attention-spiking neural network (MASNN) technique. At last, the parameter tuning process is performed through the whale‐goshawk optimization (WGO) algorithm for refining the classification performance of MASNN model. The experimental evaluation of the BCTSCM-WOA algorithm has been examined on a benchmark dataset. The extensive outcomes highlight the significant solution of the BCTSCM-WOA approach to the classification process when compared to existing techniques. 2025 2025 174 186 10.54216/JISIoT.160213 https://www.americaspg.com/articleinfo/18/show/3719