This paper introduces a first-of-its-kind hybrid approach that merges both supervised and unsupervised quantum machine learning algorithms for predicting accommodation availability in tourism datasets—something not yet explored in previous research. The research integrates supervised Quantum XGBoost (QXGBoost), Quantum Liquid Neural Networks (QLNN) into an entire real-time prediction system by connecting it with unsupervised Quantum Affinity Propagation (QAP), and Quantum Self-Supervised Learning (QSSL) functionalities in a unified framework. Market prediction together with traveler pattern analysis serve as successful use cases of classical ML approaches yet these methods encounter difficulties when dealing with tourism data that shows high-dimensional characteristics and continuous transformations. Quantum models experience faster processing and execution because they leverage superposition and entanglement technologies for complex large-scale input computation. The predictive system functions through supervised location analysis together with revenue and visitor count, understanding the ratings and performing unsupervised clustering for concealed tourist pattern identification. The testing verifies that Quantum Machine Learning offers better performance than conventional approaches together with heightened processing speed. This study develops essential methods for real-time recommendations systems at the same time it creates scalable quantum-enhanced analytics platforms for the tourism industry.