## 8 Conclusions  
This paper reviewed several approaches for implementing empirical and machine learning models in different scenarios of urban, suburban, rural, and indoor environments to address the issue of high path loss in 5G mobile wireless communication. Some of these models have shown the capability of accurately predicting path loss in an urban, suburban, rural, and indoor environment along the mid-band and high-band frequency channels. These models can aid network planners and researchers in designing, analyzing, and evaluating various wireless technology solutions and optimizing coverage for existing and future deployments. In general, the analysis of both empirical and machine learning models for path loss prediction across the mid-band and high-band channels in different scenarios reveals the complex and dynamic nature of 5G communication networks, highlighting the need for adaptable and accurate modeling techniques to ensure efficient network planning, deployment, and optimization. In conclusion, our comprehensive review and analysis delved into the performance of path loss models across mid-band and high-band frequency spectrums, specifically within the context of 5G communication networks. One significant aspect of our investigation focused on the development and evaluation of machine learning models designed to predict path loss under various scenarios. These machine learning models were rigorously compared to conventional empirical models to gauge their predictive capabilities. Our findings illuminated a compelling trend in favor of machine learning models. They consistently demonstrated superior performance in terms of prediction accuracy and computational efficiency when compared to their empirical counterparts. This marked a significant breakthrough, suggesting that machine learning models have the potential to offer a novel and promising approach for path loss prediction, particularly in high-band frequency channels. Nonetheless, our analysis is just the initial step in uncovering the full potential of these machine learning models. Substantial further research is warranted to confirm and extend these findings, encompassing a broader spectrum of scenarios, including those within the mid-band frequency range. Moreover, it is essential to explore different propagation conditions to better understand the applicability of machine learning in various real-world contexts. This review underlines the integral connection between path loss models and channel models, including considerations of slow and fast fading, in wireless communication. Path loss models play a pivotal role in shaping comprehensive channel models by quantifying the attenuation of signals over different frequencies and in diverse environmental scenarios. These models, as showcased in our study, offer critical insights into the complex and dynamic nature of 5G communication networks. By accurately predicting path loss, they form the foundation for understanding the effects of signal fading, both slow and fast, which are fundamental aspects of channel modeling. Our review emphasizes the emerging significance of machine learning models in enhancing path loss prediction accuracy, particularly in high-band frequency channels, further enriching the sophistication of channel models. As we advance, it becomes clear that bridging the gap between path loss and channel models is essential for the evolution of 5G and future wireless communication networks, enabling more efficient network planning, deployment, and optimization in the face of dynamic propagation conditions. Future work will focus on tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model capable of predicting path loss in the worst-case scenarios of the urban environment with more obstructions and irregular layouts of buildings. **Author Contributions:** Writing--original draft preparation, review, and editing, F.E.S. ; supervision, review, and editing, E.N.O. ; supervision and review, N.S. and S.S.O.