Microstrip patch antenna (MPA) single-element, half-array, and multiple-input multiple-output (MIMO) designs, with an overall MIMO antenna size of 32.02 × 32.02 mm2, are built and analyzed in this study. The bandwidth of 4.12 GHz and the band of frequencies 25.605–29.727 GHz are the values used in the simulation. Rogers RT/duroid 5880, a low-loss dielectric material, is used in the antenna's fabrication. This material has a dielectric constant of 2.2, a tangent loss of 0.0009, and an ultrathin height of 0.8 mm. The analogous model of the proposed MPA is constructed using an advanced design system (ADS) in contrast to the reflection coefficient gained using CST. The return loss of the prototype is also measured and is in contrast to the return loss seen in CST simulations. In the end, 120 data samples are collected through the simulation by using CST MWS, and five machine learning (ML) approaches, such as Nonparametric regression, random forest regression (RFR), decision tree regression (DTR), support vector regression model (SVRM), and extreme gradient boosting (XGB) regression, are implemented to evaluate the efficiency of the patch antenna. Mean square error, mean absolute error, root mean square error, R square, and the variance score are used to assess the efficacy of the five ML models. Nonparametric Regression outperforms the other ML models in terms of prediction accuracy (MSE = 2.31%, MAE = 1.77%, RMSE = 3.33%, R square = 94.28%, and var. score = 95.62%). This article describes analyzing the constructed antenna's performance by a combination of modeling, measurement, building the RLC equivalent circuit model, and incorporating machine learning approaches.