In modern Wireless Communication Systems (WCS), operational frequencies are assigned based on the increasing demands in network resources by Equipment Users (EUs) and advancements in User Equipment (UE). Higher frequencies within the context of 5G, which operate between 3 GHz–30 GHz, have been explored and exploited in the Non-Stand Alone (NSA) structures to satisfy these EUs and UE demands. The Stand Alone (SA) structure of 5G, which is expected to operate between 30 GHz to 300 GHz, has been highly challenging. In WCS, high frequencies are associated with an increase in interference anomalies due to densification and high signal attenuation caused by high-speed short waves. In this light, this research adopted the use of a hybridized method for seamless data transmission of Naïve Bayes (NB) and Support Vector Machine (SVM) algorithms in comparisons with the individual NB and SVM algorithms. The results showed NB-SVM obtained a data sensitivity of 1.000, a specificity of 0.9545, and an accuracy of 98%. Whilst individual SVM achieved data sensitivity of 0.9834, specificity of 0.8930, and accuracy of 98%. The individual NB also obtained a data sensitivity of 0.9733, a specificity of 0.8654, and an accuracy of 94%. This is aimed at combating interference anomalies in higher frequencies and increasing the efficacy in bandwidth utilization, maximizing throughput to eliminate hazardous signals.