<p>A huge national budget is required for flood damage reduction projects; thus, it must be ensured that the public money utilized therein is spent effectively and efficiently. In this context, reliable flood damage assessment is pertinent to analyzing the economic aspects of projects related to flood damage. Riverine floods cause significant damage to agriculture, businesses and infrastructure, thus adversely affect economies. The research aims to propose a GIS based methodology for monetary estimation of flood losses due to the 2022 flood and forecasted flood of 2032 in the lower Indus basin. For this purpose, the study utilizes an ensemble machine-learning model for flood susceptibility mapping. The ensemble model utilizes hybrid bagging boosting technique to integrate four independent machine learning models namely, Random Forest (RF), Logistic Model Tree (LMT), Naïve Bayes Tree (NBT), and Reduced Error Pruning Tree (REPT). The 14 flood conditioning factors considered for the research possess topographic, geo-environmental, and human-induced features. The inundation maps hence acquired by utilizing the ensemble model are further employed for the calculation of flood depth and extent to finally assess the associated economic damages. For each flooded area, the computed water surface elevation grid was compared with the ground surface elevation grid (DEM) by subtraction, resulting in a grid that represented floodwater depth (m) above the ground surface. Monetary damage to agricultural crop, residential buildings, industrial assets and infrastructure were calculated by employing unit price of the exposed elements. The data was overlaid on the estimated model’s flood depth and total estimated damages were thus obtained by utilizing the identical grid as that of the model generated flood inundation. Among the losses of 2022 flood episode, agricultural crops suffered a total loss of PKR 2.28 billion, industrial losses were PKR 240.40 million, PKR 4.93 million worth of residential units were affected and infrastructure valued PKR 145.72 million was damaged. For the forecasted flood of 2032, PKR 346.83 billion worth of agricultural crops are expected to be damaged, if proper flood mitigation strategies are not timely adopted. The novelty of this research is that it utilizes inundation depths calculated by the hybrid bagging boosting decision trees ensemble model and uses it for evaluation of economic value of the losses. The proposed technique for the assessment of flood damages can assist in decision-making while evaluating the economic feasibility of flood damage reduction projects.</p>