Accurate measurement of soil moisture is critical for precision agriculture, especially measuring with the Internet of Things (IoT)-based sensor networks for real-time soil monitoring. However, soil organic-derived amendments such as biochar can significantly alter soil physical and dielectric properties, potentially affecting the calibration and performance of soil moisture sensors. This study investigated the influence of biochar on the performance and calibration of three different soil moisture sensors (TDR 310H, Drill and Drop and CS655) under controlled laboratory conditions, with volumetric water content (VWC) measured using an Internet of Things (IoT)–based soil moisture sensor system for data acquisition and transmission. Soil sample was amended with four biochar rates of 0, 1, 3, and 5% (weight/weight) relative to soil dry weight, each was adjusted to different levels of volumetric water content (VWC) including 5, 10, 12, 14, 16, 18, 20, 25% and 30%. At each moisture level, three sensor readings were recorded and compared against standard gravimetric measurements to evaluate their accuracy. The results indicated that biochar significantly influenced sensor responses, particularly at higher application rates, due to alterations in bulk density, porosity, and dielectric properties of the soil. Sensor readings were strongly correlated with biochar levels, with coefficient of determination (R2) of 0.974 for TDR, 0.963 for Drill and Drop, and 0.989 for CS655. These high R2 values indicate that a large proportion of variability in sensor readings is explained by changes in biochar content, reflecting strong model fit. However, the performance varied across the three sensors, each requiring specific calibration adjustments depending on the biochar concentration. Before calibration, all the sensors overestimated soil water content at both 3% and 5% biochar application rate, whereas after calibration, the linear model provided the best fit, achieving the highest R2 and the lowest root mean square error (RMSE) and mean absolute error (MAE) for all sensors compared to power and exponential models. This study highlighted the importance of amendment-specific and sensor-specific calibration to ensure reliable soil moisture estimation in biochar-amended soils for future IoT applications in smart agriculture.