<p>Pakistan continues to face challenges in multidimensional poverty, especially in rural–urban and regional divides. But there are concerns about the reliability and policy relevance of poverty estimates when different methods are used. This research examines the impact of different approaches to measuring multidimensional poverty in Punjab, particularly on the estimates and spatial distribution of poverty. Household data from the Pakistan Social and Living Standards Measurement (PSLM) Survey 2019–2020 is used, with three methods: Multidimensional Poverty Index (MPI) using equal and unequal weights, Principal Component Analysis (PCA), and the Sum-Score method. The analysis uses a consistent list of indicators in the areas of health, education and living standards, and a 33% cut-off for deprivation to determine poor households. The results demonstrate the sensitivity of poverty estimates to different methods. The MPI with unequal weights estimates the highest poverty incidence (16%) followed by the Sum-Score method (15%) with both PCA and MPI with equal weights showing lower poverty incidence estimates (12%). There are clear disparities between rural and urban areas across all methods, with higher poverty in rural areas. Geographically, South Punjab reports the highest incidence of poverty (29% of households are poor), and the highest intensity of poverty (0.49) which measures the average level of deprivation of the poor population, suggesting higher levels of poverty. Variation across methods is due to different weightings and indicator distributions. The findings show that the measurement of multidimensional poverty is sensitive to the approach used, affecting the estimation and identification of poverty. The use of multiple methods can enhance the poverty analysis and inform better targeted region-specific policies.</p>