This study presents the development and characterization of 2D ferroelectric field-effect transistors (FeFETs) using nanoscale ferroelectric HfZrO₂ (HZO) and 2D semiconductors, specifically SnS₂. The fabricated FeFETs demonstrated multi-level data storage capabilities and successfully emulated essential biological characteristics, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC), Pair-Pulse Facilitation (PPF), and Spike-Timing Dependent Plasticity (STDP). The device exhibited robust stability with 10⁷ switching cycles, excellent linearity, and a high G_max/G_min ratio (>10⁵), essential for multi-level data states (>7-bit operation). When integrated into a neural network, the device achieved a pattern recognition accuracy of approximately 94% on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset, demonstrating its potential as an effective component in neuromorphic systems. The device also showed sub-femtojoule (48 aJ/spike) energy efficiency and a fast response time (1 μs), which is 10⁴ times faster than human synapses (≈10 ms). The results highlight the potential of nanoscale ferroelectric and 2D materials in building the next generation of artificial intelligence technologies. The study also explores the synaptic characteristics of the SnS₂/HZO transistor, demonstrating its ability to mimic biological synapses through features such as long-term potentiation and depression, PPF, and STDP. The device's high linearity, stability, and performance make it a promising candidate for neuromorphic computing applications. The study concludes that the SnS₂ and HZO structures can be attractive candidates for synaptic architectures in neuromorphic computing due to their high performance and energy efficiency.This study presents the development and characterization of 2D ferroelectric field-effect transistors (FeFETs) using nanoscale ferroelectric HfZrO₂ (HZO) and 2D semiconductors, specifically SnS₂. The fabricated FeFETs demonstrated multi-level data storage capabilities and successfully emulated essential biological characteristics, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC), Pair-Pulse Facilitation (PPF), and Spike-Timing Dependent Plasticity (STDP). The device exhibited robust stability with 10⁷ switching cycles, excellent linearity, and a high G_max/G_min ratio (>10⁵), essential for multi-level data states (>7-bit operation). When integrated into a neural network, the device achieved a pattern recognition accuracy of approximately 94% on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset, demonstrating its potential as an effective component in neuromorphic systems. The device also showed sub-femtojoule (48 aJ/spike) energy efficiency and a fast response time (1 μs), which is 10⁴ times faster than human synapses (≈10 ms). The results highlight the potential of nanoscale ferroelectric and 2D materials in building the next generation of artificial intelligence technologies. The study also explores the synaptic characteristics of the SnS₂/HZO transistor, demonstrating its ability to mimic biological synapses through features such as long-term potentiation and depression, PPF, and STDP. The device's high linearity, stability, and performance make it a promising candidate for neuromorphic computing applications. The study concludes that the SnS₂ and HZO structures can be attractive candidates for synaptic architectures in neuromorphic computing due to their high performance and energy efficiency.