An extensive experimental and theoretical study of two-phase flow behavior in reduced-gravity conditions has been performed as part of the current research and the results of the same are presented in this thesis. The research was undertaken to understand the behavior of two-phase flows in an environment where the gravity field is reduced as compared to that on earth. The goal of the study was to develop a model capable of predicting the flow behavior. An experimental program was developed and accomplished which simulated reduced-gravity conditions on earth by using two liquids of similar density, thereby decreasing the body force effect akin to actual reduced-gravity conditions. The justification and validation of this approach has been provided based on physical arguments as well as comparison of acquired data with that obtained aboard parabolic flights by previous researchers. The experimental program produced an extensive dataset of local and averaged two-phase flow parameters using state-of-the-art instrumentation. Such data were acquired for a wide range of flow conditions at different radial and axial locations in a 25 mm inner diameter test facility. The current dataset is, in the author’s opinion, the most extensive and detailed dataset available for such conditions at present. Analysis of the data revealed important differences between two-phase flows in normal and reduced-gravity conditions. The data analysis also highlighted key interaction mechanisms between the fluid particles and physical phenomena occurring in two-phase flows under reduced-gravity conditions. The interfacial area transport equation (IATE) for reduced-gravity conditions has been developed by considering two groups of bubbles/drops and mechanistically modeling the interaction mechanisms. The developed model has been benchmarked against the acquired data and the predictions of the model compared favorably against the experimental data. This signifies the success achieved in modeling the phenomena observed and strength of the constitutive relations (fluid particle interaction mechanisms) that were modeled. A novel flow regime identification method using neural networks was also implemented for reduced-gravity two-phase flows and found to be effective.