The information on plasma pressures in the outer part of the inner magnetosphere is important for simulations of the inner magnetosphere and the better understanding of its dynamics. Based on 17-year observations from both CIS and RAPID instruments onboard the Cluster mission, we used machine- learning-based models to predict proton plasma pressures at energies from ~40eV to 4MeV in the outer part of the inner magnetosphere (L*=5-9). The location in the magnetosphere, and parameters of solar, solar wind, and geomagnetic activity from the OMNI database are used as predictors. We trained several different machine-learning-based models and compared their performances with observations. The results demonstrate that the Extra-Trees Regressor has the best predicting performance. The Spearman correlation between the observations and predictions by the model data is about 68%. The most important parameter for predicting proton pressures in our model is the L* value, which is related to the location. The most important predictor of solar and geomagnetic activity is the solar wind dynamic pressure. Based on the observations and predictions by our model, we find that no matter under quiet or disturbed geomagnetic conditions, both the dusk-dawn asymmetry at the dayside with higher pressures at the duskside and the day-night asymmetry with higher pressures at the nightside occur. Our results have direct practical applications, for instance, inputs for simulations of the inner magnetosphere or the reconstruction of the 3-D magnetospheric electric current system based on the magnetostatic equilibrium, and can also provide valuable guidance to the space weather forecast.