One of the key tasks in wireless sensor networks is the integrated estimation of parameters of a radiation source under observation. To improve accuracy, data from multiple sensors are combined, with each sensor providing an independent local estimate of the source’s parameters, while the final estimate is generated at the central node. The accuracy of the final estimate depends heavily on the algorithm used in the central node, which should account for the statistical characteristics of the individual sensor estimates. This paper introduces new algorithms for the integrated estimation of radiation source parameters under various conditions (e.g., continuous and discontinuous parameters, presence or absence of anomalous errors) and identifies the conditions for their optimal performance. We perform statistical synthesis of integrated estimation algorithms based on common models of parameter estimate distributions. For each model, two estimation algorithms are developed: the first is based on statistical hypothesis testing, while the second uses parameter estimation for discrete observed data. We conduct statistical simulations to evaluate the impact of key factors such as the number of quantization bits, the signal-to-noise ratio, and the number of sensors in the network. The proposed algorithms can enhance radiation source parameter estimation accuracy in wireless sensor networks and facilitate optimal network configuration for maximum efficiency under given constraints.