As the development of 5G technology is becoming faster and rapid, predicting coverage areas accurately for optimization of network performance and ensuring connectivity has become important. This paper elaborates various machine learning algorithms used in the prediction of 5G coverage using RF Signal Data. For estimating accuracy levels in various models, Target variable, Bandwidth has been considered. Traditional models include Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), LightGBM, AdaBoost, Bayesian Network Classifier, Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and so on. Such traditional models are evaluated with further advanced techniques such as stacking, voting classifiers, convolutional neural networks, etc. The aim is to identify the most influential features in the 5G coverage prediction. Through comparative analysis, the performance of these models is benchmarked to outline strengths and weaknesses within each approach. Findings from this research suggest that ensemble methods, particularly Stacking and Voting Classifiers, as well as CNN, possess higher prediction accuracy and robustness that would be helpful for 5G network planning and deployment.