Type 2 Diabetes Mellitus (T2DM), a prevalent metabolic disorder characterized by insulin resistance and impaired secretion, is treated with five FDA-approved medications, namely sulfonylureas, meglitinides, biguanides, thiazolidinediones, α-glucosidase inhibitors, and dipeptidyl peptidase-4 (DPP-4) inhibitors. However, these drugs have adverse effects, prompting researchers to investigate DPP-4 inhibitors (gliptins) for their improved safety profiles. In this study, triazolopiperazine derivatives were developed as DPP-4 inhibitors using the Schrödinger Suite 2022-1, integrating field-based Quantitative Structure Activity Relationship (QSAR), molecular docking, and molecular dynamics (MD) simulations. Forty-five triazolopiperazine derivatives with IC50 values from 0.43 nM to 455 nM were used to develop spatial characteristics via Gaussian field based QSAR, yielding promising statistical results. Contour maps revealed favorable and unfavorable interactions around the triazolopiperazine nucleus, leading to the design of 10 new compounds with improved predicted IC50 values, strong binding affinities, and docking scores. Compound G86 demonstrated superior binding affinity and inhibitory activity against DPP-4 compared with sitagliptin, suggesting its potential for further development. QikProp analysis predicted favorable pharmacokinetic and physicochemical properties and a better safety profile for G86, whereas MD simulations showed that the G86-1X70 complex was more stable than the sitagliptin-1X70 complex. This study highlights the effectiveness of computational methods in accelerating anti-diabetic drug discovery and facilitates future research and therapeutic advancements in T2DM management.