There is a constant issue of identifying missing persons whose only identification resource present is that of their old profile or photo or sketch with very vague features, posing a challenge in identifying the person. One profile or photo or sketch would be insufficient in efficiently identifying through age progression because facial features of a person have been proven to change over time in different environments. Facial age progression is a challenging task in the field of image-based regression where multiple factors are put into consideration, such as genetics and lifestyle changes, to accurately predict future appearance. The work done in this paper presents a workflow to accomplish age progression of faces along with variations to achieve possible permutations of faces that could occur depending on external conditions. To summarize the results, it would comprise a three-dimensional outlook. One dimension would represent time, one dimension would represent variants, and the final dimension would represent the generational variance brought by genetic algorithm. From a Blackbox perspective, the input would be a source image, source age, gender, race, and target age/s, and the output would be multi-variant age-progressed faces based on the input source image. The target area of application would be in forensics rather than its commonly used purpose, entertainment and fun.