Prolactin is mainly secreted by the anterior pituitary and is able to stimulate mammary gland development and lactation in mammalians. Although prolactins share a common ancestral gene encoding, they show species specific characteristics and their efficiency may be different in various mammals. The importance of protein structures of all sequences of this hormone have been studied by various bioinformatics algorithms. The results showed bioinformatics tools and modeling methods can be used to identify the species specificity of prolactin hormones in animals with an acceptable precision rate. Based on the author’s knowledge, this is the first report on the structural variation of prolactin hormones by specific structural protein features. Gain ratio model acquired the best accuracy and performance among the algorithms applied here and can be used on similar proteins. The counts and the frequencies of dipeptides were the most important protein attributes in this regard. It has also been reported here that feature selection or attribute weighting can be used to select the most important protein attributes and to reduce the burden of processing equipment. The new findings presented here open up new windows in understanding the characteristics of prolactin hormones and also pave the way to engineer more efficient hormones by using various mutagenesis tools such as site directed mutagenesis.