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Defect density and its migration under the influence of an applied electric field plays a crucial role for memristors toward designing a fundamental element of neuromorphic computing, e.g., an artificial synapse. Therefore, to have tunable performance, even at the nanoscale, it is essential to have better control over the defect density, albeit achieving it with direct growth methods remains a challenge. Here, we demonstrate an effective and robust approach to tune the defect density and consequently resistive switching behavior in TiO x layers using low energy argon ion implantation as a tool. Interestingly, by selecting appropriate implantation parameters an adjustable nanoscale resistive switching property, in terms of increased memory window and lower operative voltage, is achieved. These findings are attributed to ion-beam induced controlled defect engineering and are well supported by our experimental results and the predictions of Monte Carlo simulation results. Further, learning ability with an input electric stimuli strongly depends on the implantation fluence, revealing a striking similarity with biological synapses. The present study provides a one-step processing to tune the defect density and in turn resistive switching behavior for designing advanced memory devices and neuromorphic computing.