Efficient deployment of AI models on edge devices requires balancing predictive accuracy with resource constraints. This chapter presents a comparative study of neural network pruning strategies, evaluating their impact on accuracy, sparsity, and computational efficiency. By systematically analyzing different pruning approaches—such as weight pruning, structured pruning, and hybrid strategies—the authors highlight trade-offs between compression rate and performance. The results provide practical guidance for selecting pruning techniques suited for edge environments, supporting reliable and resource-efficient AI deployment in real-world applications.