The rise in Precipitation frequency and intensity due to climate change has led to increased surface runoff, affecting many regions previously considered safe from flooding. Comprehending hydrological dynamics of a watershed is a critical aspect of efficient flood management. In this study a Soil Conservation Service-Curve Number (SCS-CN) method, integrating Geographical information system (GIS) and Remote Sensing (RS) was used to estimate surface runoff in Chikwawa District, Malawi. The study utilized slope, Hydrologic Soil Group (HSG), land use/land cover (LULC), and rainfall data to generate curve numbers (CN) and surface runoff estimates. The region is predominantly characterized by HSG categories C and D, with only limited areas classified under soil group A. Land cover is mainly Bare land (27.93%), followed by Forest (19.90%), Vegetation (17.49%), Agricultural land (14.03%), Built-up areas (11.99%), and Water bodies (8.67%). The study utilized rainfall data from March 2023, with antecedent rainfall levels ranging from 55 to 320 mm, categorizing the conditions under Antecedent Moisture Content (AMC) III. Curve Number (CN) values were calculated by overlaying land use with soil hydrologic groups, incorporating adjustments for slope and initial abstraction. Results indicated that bare land exhibited the lowest initial abstraction, whereas forested areas demonstrated the highest. The total estimated runoff for the period reached 1,036.276 × 10⁶ m3, with an average runoff depth of 0.312 m. The analysis revealed that the region’s high runoff susceptibility is largely driven by the presence of bare land and HSG classifications C and D, resulting in reduced infiltration rates and elevated runoff, particularly during extreme precipitation events like the March 2023 floods.