The 802.11 ac protocol is widely utilized in local-area-networks with wireless access (WLANs) because of its effective 5GHz networking technology. Several path-loss and link-speed (LS) prediction models have previously been employed to aid in the effective design of 802.11 WLAN systems that predict the received-signal-strength (RSS), and LS between the client and the access-point (AP). However, majority of them fail to account for numerous indoor propagation phenomena that affect signal propagation in complex environments. This includes the shadowing that influences RSS, especially in a network system with multiple moving parts and small-scale fading, where signal reflections, obstacles, and dispersion lead to RSS fluctuations. Therefore, taking into account shadow fading influence in the LS estimation model is critical for enhancing estimation accuracy. Previously, we proposed modification of the simple log-distance model by taking shadowing variables into account which dynamically optimize the RSS and LS estimation precision of the previous model. Though our modified model outperforms the prior model, the model’s accuracy has not been evaluated in comparison to a wide range of other mathematical models. In this paper, we present the performance investigation of various estimation models for RSS and LS estimations of 802.11ac WLANs under various scenarios and analysis their performance accuracy by considering several statistical error models.  To test its relative effectiveness the proposed modified model's performance is also compared against two existing machine learning (ML) approaches. To calculate the models parameters including shadowing factor, we first show the experimental results of RSS and LS of the 802.11ac MU-MIMO link. Then, we tune the path-loss exponent, shadowing factors, and other parameters of models by taking into account experimental data. Our estimation results indicate that our modified model is more precise than the other mathematical estimation models and its accuracy is very similar to the random forest (RF) ML model, in an extensive variety of consequences with less error.