This paper explores multi-objective stochastic fuzzy linear programming problems using picture-fuzzy theory to model parameter uncertainty and fuzziness. Picture-fuzzy theory provides a flexible way to handle uncertain data by representing acceptance, rejection, and hesitation degrees. Our study introduces a method to transform the initial stochastic fuzzy problem into a quasiconvex programming problem. Our study enhances computational efficiency and ensures algorithm convergence by applying a gradient descent approach rather than conventional heuristic methods. Our study provides theoretical proof using quasiconvex optimization to validate the proposed method, establishing a foundation for its convergence and effectiveness. To illustrate the method’s feasibility and efficacy, the paper presents computational examples demonstrating its correctness and potential applications, particularly in economics and finance where uncertainty and fuzziness in market data are significant. The research opens new pathways for solving complex programming problems in uncertain environments.