The development of computational tools that permit microstructurally-based predictions for tensile and fracture toughness properties of commercially important titanium alloys is a valuable step towards the accelerated maturation of materials. Modeling tools, such as Neural Network Models based on Bayesian statistics have been used to predict the toughness of Ti-6Al-4V at room temperature. The development of such rules-based models requires the population of extensive databases containing compositional and microstructural information. These databases have been used to train and test Neural Network models. These models have been successfully used to identify the influence of individual microstructural features on the mechanical properties, consequently guiding the efforts towards development of more robust phenomenological models. The influence of the individual microstructural features on toughness have been subsequently probed using a variety of characterization techniques, including orientation microscopy and transmission electron microscopy. These results will be discussed.