In this work, we introduce the concept of fuzzy temperature indices and rigorously compute them for fundamental fuzzy graph structures as well as their standard operators, including Cartesian products and compositions. Utilizing a graph-theoretical modeling approach, fuzzy graphs are constructed for a selection of pharmaceutical compounds commonly prescribed for Autism Spectrum Disorders (ASD), specifically Aripiprazole, Haloperidol, Risperidone, Sertraline, Venlafaxine, and Ziprasidone. The fuzzy temperature indices are then derived based on the underlying physicochemical descriptors of these compounds. Linear regression analyses are performed to explore the predictive capacity of the fuzzy topological indices in relation to drug properties. The findings highlight the significance of fuzzy temperature indices as robust mathematical invariants, providing valuable insights into the structural and functional profiling of ASD medications, and opening new avenues for the application of fuzzy graph theory in pharmaceutical sciences and computational drug design.