This noise is characterized by its randomness and uniform energy distribution throughout the frequency spectrum. White Gaussian noise is a common model for representing random background noise in communication systems. By predicting noise levels, one can more effectively optimize system parameters and frequency allocation [11]. The distance between users is an important factor that influences signal quality and, consequently, spectrum efficiency. In spectrum sensing, the strength of the received signal depends on the distance between the transmitter and receiver. Predicting these distances helps to adjust the transmission power levels: users closer to the transmitter require lower power for reliable communication, while users farther away need higher power [12]. This power control optimizes energy efficiency and minimizes interference. Especially in systems with limited resources, such as bandwidth, predicting distances is fundamental for effective resource allocation. Users closer to the transmitter can be assigned greater bandwidth and consequently higher data rates, while those farther away might be allocated smaller bandwidths to improve the signal quality at the same transmission power level. Additionally, distance prediction is essential for providing location-based services. The distance between users can also impact the performance of artificial intelligence models used to increase spectrum efficiency. By estimating distances, services like navigation, location-based advertisements, and emergency services can be offered. To predict noise and distances between users, regression models and deep learning architectures are recommended [13]. Regression models are a class of machine learning algorithms designed to predict continuous numerical values based on input data. These models play a crucial role in various fields, including economics, finance, healthcare, and natural sciences. They allow for the analysis and forecasting of trends, relationships, and outcomes by learning from historical data. Among the most common traditional machine learning regression approaches are support vector regression (SVR) (an extension of support vector machines to regression), decision trees and random forests [14], linear regression (one of the simplest yet most widely used regression techniques), and ridge and lasso regression (variants of linear regression). In more robust regression approaches, convolutional neural networks (CNNs), initially designed for image analysis, can be adapted for regression tasks [15]. More recently, transformer, designed for forecasting, has shown promise for regression activities [16]. In this article, we propose the use of regression models to predict noise levels and distances based on spectrum sensing signals. During our study, we generated a dataset that considers important parameters, including a wide range of noise power densities, an extensive sensing area, and power leakage from the PUs. We compared both traditional and deep learning models for prediction purposes. Furthermore, we evaluated the results using various metrics. Our proposed method has shown promising results, with a correlation coefficient exceeding 0.98 for noise and over 0.82 for distance. Additional metrics also indicate that our method is effective in predicting noise levels and distances between users. Such predictive capability can be important in designing communication systems to enhance spectrum efficiency. This article is organized as follows: in Section 2, related works about the prediction of noise levels and distances between users in a communication system are presented; in Section 3, the proposed methodology for data generation, training, and evaluation is described; in Section 4, the experiments conducted and the results achieved are presented; the discussions related to the experiments are presented in Section 5; lastly, in Section 6, the conclusions are presented.