The frequency and intensity of flooding have been increasing in urban watersheds. Urbanization disrupts natural landscapes by replacing vegetated areas with impervious surfaces, reducing infiltration and increasing runoff. The objective of this study was to evaluate the relationship between change in impervious surface area and runoff amount of Mihang’o watershed located in the outskirts of Nairobi for the period 2000–2022. The specific objectives of this study were as follows: To determine the change in the impervious surface area of Mihang’o watershed, the trend of precipitation amount in the watershed, and the trend in runoff amount, a major source of flood water from the watershed. Supervised classification was performed on land satellite (Landsat) images to determine percentages of impervious surface cover for the study period, and linear regression analysis was used to establish the trend. Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data were retrieved from Google Earth Engine, then processed to produce monthly and annual rainfall totals, and Mann–Kendall trend tests were used to establish the rainfall trend for the watershed. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was used to simulate runoff from the watershed with the rainfall data and impervious surface area percentages as inputs; then, linear regression analysis was performed to establish the runoff trend. The impervious surface area increased by 87.03% from 2.88% (0.49 km2) of the total surface area of the watershed in 2000 to 22.21% (3.91 km2) in 2022, demonstrating an approximate increment of 3.96% (0.88 km2) each year. The Mann–Kendall trend test results (Sen’s slope results [β = 0.832], Kendall’s tau results [τb = 0.146], and p-value [0.625]) confirmed that there is no significant change in rainfall amounts. Runoff increased by 84.75% from 0.18 mm in 2000 to 1.18 mm in 2022; otherwise, an approximate increment of 3.85% (0.045 mm) was evident each year. Besides the impervious surface area, the HEC-HMS model factors in the length of slope, length of reach, soil type, size of subbasins, and longest flow path, thus producing accurate runoff estimations.