Several artificial photo-synaptic devices have recently emerged to replicate photonic synaptic plasticity for neuromorphic computing. The integration of an artificial biological neuromorphic vision sensor and a photodetector into a single device poses a notable challenge. Here, for the first time, a novel photo-synaptic memristor based on monochalcogenide GeS was developed through defect engineering. The fabricated device exhibited rapid photo-response and persistent photoconductivity behavior with both pristine and defect-engineered GeS, owing to its distinct trapping state relaxations. First Principles based DFT calculations reveal that additional energy states (acting as photon traps) are present in GeSOx. GeS and GeSOx possess an indirect energy bandgap of 1.62 and 1.30 eV, respectively. Consequently, these devices could generate photocurrent upon light exposure, mimicking neuronal behavior. Moreover, they exhibit essential synaptic functionalities, such as STM, LTM, EPSC, PPF, and transition from short-term memory to long-term memory. Particularly, the device exhibits outstanding image memory and letter recognition optical wavelength sensitive responses, mimicking the biological retina. The simulated machine vision system with the GeS retina device as the processing core presents excellent accuracies of 96.75 textpercent for MNIST and 85.43 textpercent for fashion-MNIST datasets. Thanks to the photosensitivity of GeS, these devices can operate at low bias voltage of 0.1 V and consume only ~85 pJ of energy per usage. Furthermore, the logic function “AND” was incorporated into the optoelectronic simulation. The findings of this study present the way for the integration of sophisticated robotic vision systems and advancing neuromorphic computing capabilities.