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Nano and microfluidic technologies have shown great promise in the development of controlled drug delivery systems and the creation of microfluidic devices with logic-like functionalities. Here, we focused on investigating a droplet-based logic gate that can be used for automating medical diagnostic assays. This logic gate uses viscoelastic fluids, which are particularly relevant since bio-fluids exhibit viscoelastic properties. The operation of the logic gate is determined by evaluating various parameters, including the Weissenberg number, the Capillary number, and geometric factors. To effectively classify the logic gates operational conditions, we employed a deep learning classification to develop a reduced-order model. This approach accelerates the prediction of operating conditions, eliminating the need for complex simulations. Moreover, the deep learning model allows for the combination of different AND/OR branches, further enhancing the versatility of the logic gate. We also found that non-operating regions, where the logic gate does not function properly, can be transformed into operational regions by applying an external force. By utilizing an electrical induction technique, we demonstrated that the application of an electric field can repel or attract droplets, thereby improving the performance of the logic gate. Overall, our research shows the potential of the droplet-based logic gates in the field of medical diagnostics. The integration of deep learning classification algorithms enables rapid evaluation of operational conditions and facilitates the design of complex logic circuits. Additionally, the introduction of external forces and electrical induction techniques opens up new possibilities for enhancing the functionality and reliability of these logic gates. [ABSTRACT FROM AUTHOR]