Radio Frequency CMOS Narrow band Low noise amplifier (LNA) design is a challenging task to achieve target specification as it has to satisfy significant constraints, especially in lower technology nodes. The LNA Characteristics are susceptible to the variation of circuit elements commonly performed by Meta-heuristic algorithms for finding globally optimal solutions by parametric optimization. However, their performance is limited because their cost function relies on traditional square law expressions. To mitigate this problem, the Particle Swarm Optimization (PSO) algorithm is combined with Artificial Neural Network (ANN) model to provide the solution. Firstly, an ANN surrogate model represents a time-consuming simulation model in the optimization process of Cascode CMOS Low noise Amplifier design. The optimal design parameters of the proposed model are validated with numerical simulation using Cadence Virtuoso to achieve significant gain and minimum noise figure. The results demonstrate the proposed model’s effectiveness in designing high-performance Low noise amplifiers.