The water’s electrical conductivity (EC) is typically measured by the amount of contaminants or dissolved particulate matter present. It measures the amount of saltiness in water and is influenced by temperature. In short, the ability of a solution to pass current with its ionic process is measured by EC. As per WHO guidelines, the EC should not exceed 400 μS/cm. The relative state or health of the water body and its associated biota can be declined due to significant changes or increased conductivity. Moreover, the measure of conductivity provides an overview of a water supply’s hygienic status and is considered one of the vital water quality parameters. In this study, a closed-form expression is derived from artificial neural network modeling of EC from climate parameters such as precipitation and temperature; and land use parameters such as urban, forest, agriculture, grassland, and shrub land use factors of Krishna River Basin. The EC data is obtained from Andhra Pradesh Pollution Control Board (APPCB), and the climate data is obtained from Indian Meteorological Department (IMD). The same procedure can be used to derive expressions for other water quality parameters of Krishna River Basin’s water quality modeling, analysis, and predictions.