The design of secure and efficient stream ciphers heavily relies on the use of the Boolean functions, which affect the nonlinearity, correlation immunity, and algebraic complexity. The current paper gives a detailed approach to optimization of Boolean functions which combines classical strategies with recent techniques of artificial intelligence. Classical methods, such as Karnaugh maps, QuineMcCluskey minimization, and ReedMuller spectral analysis are compared based on their use in optimal cryptographic properties. Nonetheless, such techniques are usually limited in the scaling of high-dimensional functional spaces. To solve this problem, AI-based optimization techniques like genetic algorithms, reinforcement learning, and swarm intelligence are investigated to improve performance and adaptiveness. The experimental performance of the scheme has shown a great enhancement in nonlinearity, balancedness, and resistance to algebraic and correlation attacks. The suggested hybrid optimization has made it possible to design robust, scalable and efficient Boolean functions in next generation stream cipher systems.