It is often difficult to predict the sex of an individual based on bloody incomplete footprints. However, such prints/impressions are particularly common in a crime scene. Again variability in the texture, color of the target surface has an impact on the bloodstained impression formed. The study of bare foot, footprint, footwear (i.e. shoe, canvas etc.) within the legal context is referred to as forensic podiatry. Based on the fact that it is possible to predict the sex of an individual from footprint impressions, an automated model has been proposed in this paper for analyzing the sex of an individual from his/her broken/incomplete footprint impressions based on morphological features alone. Five male and female volunteers aged between 20 to 65 years participated in dataset development. Keeping the blood volume constant and having stepped on differently shaped porcine blood pools, the individuals were asked to walk on herbarium sheets. The footprints were recorded and documented in accordance with the guidelines in place for physical evidence documentation within the forensic domain. The morphological features that were extracted from each of the footprint impressions are footprint length, footprint breadth, angle of walking, approximated heel radius etc. Using exhaustive cross validation technique, the dataset was divided into training and test set. Non-redundant, relevant features that are particularly effective at sex prediction were marked out using the relief algorithm in coherence with the correlation metric. Supervised learning techniques were used on the dataset to predict the sex of the owner of an unknown footprint. The study concentrates on morphological features in order to deal with bloodstain footprint transfer stains formed on any non-porous/non-absorbent surfaces such as cemented floor, glass, mosaic floor space, colored and designed tiled floor spaces. Features such as the angle of walking and foot breadth were found to be particularly influential in sex prediction from incomplete bloodstained foot sole impressions. In comparison to a system for sex prediction from complete footprint impressions (82.2%), the automated system developed on incomplete foot impressions recorded an accuracy level of 83.47%.