## 1 Introduction  
Accurate path loss prediction is important in wireless networks. Power distribution within the coverage area can be accurately ascertained during Link Budget, leading to a network with a better quality of service if the predicted values are effectively used during design, expansion, or maintenance [1, 2, 3]. Deterministic path loss models provide a reasonable accuracy but are computationally expensive and require details that include a 3D model of the environment, building materials, and so on. Empirical models are much easier to use but are prone to errors. Meanwhile, machine learning (ML) models are more accurate than empirical models and sometimes exceed the accuracy of deterministic models. Such ML models were developed with algorithms such as Multiple Layer Perceptron (MLP), Support Vector Regression (SVR), Random Forest (RF), and XGBoost [4; 5; 6]. Furthermore, a Convolutional Neural Network (CNN) is employed to extract features from images that describe the effects of environment due to the dependence of path loss on the propagation path/environment. Such images include satellite images for a wide area [7], pointwise satellite images [8], segmented images with shadings that represent heights of buildings in an urban environment [9], segmented images based on some parameters, with building height inclusive [10], and pseudoimages representing the fusion of various data types [11]. Most of the models developed are for fixed frequency, antenna height, and environment. However, since path loss varies with the environment, such model prediction will be inaccurate in other scenarios. Considering the fact that path loss prediction is performed before networks are deployed, it is reasonable to develop models that can be used to make accurate predictions for varieties of environments and network parameters. Otherwise, the model will only be suitable for the existing network it was designed for or a network with almost the same parameters in a similar environment. Although ML models make better predictions, empirical models have the advantage of being versatile as they consider a range of frequencies, distances, and antenna heights over which these models are valid. Some of these models have parameters tailored to a specific type of environment, such as COST 231 Hata [12], and Stanford University Interim (SUI) model [13]. Therefore, there is a need to have machine learning path loss models valid for multiple parameters and environments so that they can provide accurate predictions during design and prior to network deployment. This will eliminate the cost of taking measurements under certain network parameters from individual environments and developing individual models specific to the environments and selected network parameters. [PERSON] et al. [14] developed an MLP model for the prediction of a number of frequencies in the Ultra-High-Frequency (UHF) band. The study considered the fact that the propagation path from the transmitter to receiver position varies such that some portions can be classified as either rural, urban, or suburban environments. Thus, distances covered in each type of environment were included as features for model development. The problem with such a model is that it does not capture the micro variations of the environment types along the propagation path; rather, it is developed based on distances covered in each environment type without a detailed description. [PERSON] et al. [15] developed a model valid at frequencies between 0.8 and 70 GHz. The model was developed based on the features distance, frequency, propagation scenario, and environment type. The propagation scenario could be either \"above rooftop\" or \"below rooftop\". Above rooftop is a scenario in which one of the antenna heights is above the surrounding rooftops, and below rooftop is when all antenna heights are below the surrounding rooftops.