One of the most important parameters in me- teorological data is the Precipitable Water Vapor (PWV). It can be measured by radiosonde stations (RS), but the fact is that RS are not available in all times. Therefore, GNSS satellite signals are considered an accurate function to compute it within a conversation factor. The conversa- tion factor depends on the weighted mean temperature (Tm) which is non-measurable. In this research, a new idea to estimate Tm is provided, which can potentially contribute to the GNSS meteorol- ogy. The Tm was designed, including six RS, over one year in Egypt as input parameters. The machine learning (ML) model has been utilized in the design (IBM SPSS Statis- tics 25 package). The new model needs to collect the day of year (DOY), site location information and surface temper- ature to predict the Tm . The results of ML model and four other empirical models (Bevis et al., Wayan and Iskanda, Yao and Elhaty et al. models) are compared. The validation work is carried out, using the radiosonde data, and results indicate that the new Tm model can achieve the best per- formance with RMS of 1.7 K.