Memristors, also known as artificial synapses, are devices that are able to mimic the memory functions of biological synapses. To emulate synaptic functions, memristors need to exhibit plasticity, which is a pivotal phenomenon in their biological counterparts. In a previous work, we demonstrated that geopolymers present memristive properties. In this work, we study different types of synaptic plasticity properties of geopolymer memristors. We demonstrate short-term and long-term memory resulting from potentiation-depression; Hebbian learning inspired spike-timing-dependent plasticity, spike-rate-dependent plasticity, history-dependent plasticity, paired-pulse facilitation, paired-pulse depression, and post-tetanic potentiation. These synaptic properties can be ascribed to the electro-osmosis-induced movement of ions in the capillaries and pores of the geopolymer memristors. These properties are extremely promising for the use of geopolymers in neuromorphic computing applications.