This paper presents the findings of a research on an artificial intelligence-based hybrid Support Vector Machine–Bat Optimization Algorithm (SVM-BOA) model developed for spectrum sensing in the Ultra High Frequency (UHF) television band (470 – 694 MHz). The main objective is to enhance detection accuracy, probability of detection, probability of false alarm, and computational efficiency for efficient spectrum utilization. Experimental spectrum measurements were conducted across Kano, Kaduna, and the Federal Capital Territory (FCT), Abuja, Nigeria, to assess the level of spectrum occupancy and identify available Television White Space (TVWS) channels. Relevant signal features, including received signal power level, noise power level, and frequency, were extracted from the field data collected and used to train and test the proposed model. The model was developed and implemented in MATLAB computing environment. The new model was validated using test data for classification of channel status as either free or occupied. The developed model achieved a detection accuracy of 97.67% at a threshold of −99.5dB, based on metric from confusion matrix, outperforming baseline SVM (78.57%) and other benchmark optimization-based SVM models, including Grid Search SVM (92.86%), Random Search SVM (89.29%), Genetic Algorithm SVM (92.86%), and Particle Swarm Optimization SVM (92.78%). It also attained a probability of detection (Pd) of 1.00 (100%) and a probability of false alarm (Pfa) of 0.1053 (10.53%), which satisfies the sensing performance requirements of the Institute of Electrical and Electronics (IEEE 802.22) standards under low-signal conditions. These findings suggest that the hybrid SVM-BOA approach will offer superior classification accuracy and computational efficiency relative to conventional techniques and comparable hybrid models, signifying a strong potential for the improvement of spectrum utilization in next-generation cognitive radio networks.