The aqueous electrolyte can be a deformable and stretchable liquid material for iontronic resistive memory devices. An aqueous medium makes a device closer to the brain‐like system with the movement of ions. This review paper proposes advances in liquid resistive memories and neuromorphic computing behavior to emulate electronic synapses. Primarily, the aqueous iontronic resistive memories can be used to study electrode and active layer materials and different device structures. Hence, herein, a timely and comprehensive study of these devices using ionic liquids, hydrogels, salt solutions, and soft electrodes to classify the device mechanism is presented. The filament formation is discussed in detail based on ion concentration polarization, electrode metallization, and movements of ions and charged molecules, which result in the formation of the metal dendrite. To manufacture a higher‐performance memory, device parameters should be optimized based on aqueous electrolytes, electrode materials, and other device design parameters. Aqueous electrolytes have smooth neurotransmission ability to fabricate brain‐inspired resistive memories with stable performance and device repeatability with smooth ion transmission. Aqueous electrode materials can be reliable for neural interface activities to compute electronic synapsis with electrical and chemical properties to ensure device reliability for a longer time period.

With the increase of big data and artificial intelligence (AI) applications, fast and energy‐efficient computing is critical in future electronics. Fortunately, nonvolatile resistive memory devices can be potential candidates for these issues due to their in‐computing and neuromorphic computational abilities. Hence, the paper proposes a highly flexible and asymmetric hexagonal‐shaped crystalline structured germanium dioxide‐based Ag/GeO2/ITO device for high data storage and neuromorphic computing. The proposed device shows the highly asymmetric memristor behavior at low operating voltage to block backward current. The operational behaviors are observed by modulating the applied amplitude, current compliance, and varying the frequency, which shows excellent stability and repeatability in electrical characterizations. Furthermore, the neuromorphic device exhibits synaptic learning properties such as potentiation‐depression, pulse amplification, and spike time‐dependent plasticity rules (STDP). Here, the weights update of the memristive synaptic device is analyzed using a multilayer perceptron convolutional neural network (CNN) by optimizing the learning rate, training epochs, and algorithm to achieve higher accuracy for pattern recognition using CIFAR‐10 data. Undoubtedly, the demonstrated results suggest that the proposed device is a promising candidate to develop high‐density storage and neuromorphic computing technology for wearable and AI electronics.