In a wireless sensor network (WSN), the sensing coverage property reflects the quality of service (QoS) and the effectiveness of a sensor in monitoring the desired area of interest. The deployment strategies are fundamental to achieving accurate coverage prediction, making them a critical aspect that requires specific investigation. Furthermore, to ensure maximum coverage prediction, various variables are involved in different scenarios. Among them are the type and number of nodes, deployment type, sensing range, the relationship between communication range and sensing range, the strategy used to detect full coverage, the geometrical parameters of the sensing area, and many others. The present work aims to evaluate the prediction of sensing coverage using a metamodeling strategy. A combination of a quadratic polynomial model derived from the Taylor equation and a spatial model based on a particular spline interpolation is used to predict sensing coverage (SCP) for 2D and 3D deterministic deployments. The resulting meta-model accurately describes the predicted coverage across the entire domain and interpolates values at a given location. Eventually, the study presents a comparison between the quadratic sub-model and the generated meta-model (MSE (meta-model) = 10-31 against 10-4 of quadratic for 2D and 10-12 against 10-3 for 3D). Our findings show that the latter is a powerful interpolation tool that yields smoother, more reliable results than the usual quadratic.