Agent-based microsimulation modeling techniques are adopted for urban system modeling mainly because of their capacity to address the complex interactions among individuals, households, and other urban elements. The performance of urban simulation models is largely dependent on the quality of the input data, which is generated through a population synthesis procedure. This study proposes a Bayesian network and generalized raking techniques for population synthesis. The Bayesian network is used to generate the synthetic population pool from the microsample, and generalized raking is used to fit the synthetic population with the control total. Some of the key features of the proposed population synthesis are as follows: accommodating heterogeneity based on both household and individual attributes; tackling missing/incomplete observations in the microsample; and generating a true synthesis of the population from the microsamples. A data-driven structure learning technique is adopted to generate effective and optimal structures among the heterogenous households and individuals. This Bayesian network + generalized raking procedure is implemented to generate a 100% synthetic population at the smallest zonal level of dissemination area for the Central Okanagan region of British Columbia. The results suggest that capturing heterogeneity within the Bayesian network has tremendously benefitted the reconstruction process to efficiently and accurately generate a synthetic population from the available microsample. Finally, this population synthesis is developed as a component of the agent-based integrated urban model, currently under development at The University of British Columbia’s Okanagan campus.