Abstract: The COVID-19 pandemic disrupted work systems, family, and social life. The mandatory lockdown forced employees to shift to work-from-home (WFH) setup which exposes them to WFH conflicts. This study provides a machine learning—based approach for prioritization of factors affecting WFH conflicts during the COVID-19 pandemic. These factors include time spent with the family (F1), leisure activities (F2), household task (F3), family quality of life (F4), agitation and anger from work (F5), financial obligations (F6), family presence (F7), family issues (F8), health-related (F9), and work burn-out (F10). Using the backpropagation (BP)-artificial neural network (ANN) modeling and Olden’s connection weights (CW) approach, the order of influence of these parameters to the productivity rating (PR) was observed. Based on the results, the 10-21-1 network structure is the best performing model (BPM) with correlation coefficient (R) = 0.98173 and mean squared error (MSE) of 0.02607. This network topology also provided the least Akaike Information Criterion (AIC) value showing that it is the best model. Using its connection weights (CW) through Olden’s approach, the results showed that the financial obligations are the most influential parameter (MIP) while the household task is the least influential parameter to the productivity model. The utilization of machine learning techniques proved to be effective in determining the influence of predictors on the target output. The obtained findings from the study could assist the organization and managers in resolving work-from-home conflict and productivity issues.