In the ever-evolving landscape of industrial production, the optimization of automated systems stands as a critical imperative for organizations striving to maintain competitiveness amidst the Fourth Industrial Revolution. This paper explores the role of Mixed Model Stochastic systems in achieving dynamic production in modern manufacturing environments. MMS assembly lines integrate mixed model production and stochastic modeling to enhance flexibility and efficiency while accommodating uncertainty and variability. Despite their advantages, challenges such as task balancing, sequencing, and dynamic scheduling require careful consideration. While optimization techniques and heuristic algorithms are plausible solutions for addressing these challenges, the paper focuses on the importance of Artificial Intelligence algorithms tailored for dynamic task allocation and scheduling, predictive analytics, and machine learning in optimizing human–machine collaboration within mixed model stochastic environments. Through a case study of a water bottling plant, which is currently operating in a human–machine collaboration mode, the paper examines the different AI techniques to increase production efficiency in real-world scenarios.