Acidulants are commonly used in gummy candies to enhance their flavour by intensifying specific notes and providing a tart taste. This study utilized the Python programming language, a free and open-source tool, to conduct a mixture design. This offers an accessible alternative to proprietary software and helps to bridge the digital divide between researchers and companies with different financial capacities. The main objective was to develop a Python script to implement a simplex-lattice mixture design to optimize the sourness, flavour, and overall acceptance of lemon-flavored gummy candies. The methodology involved creating mixture designs and analysing the impact of citric, malic, and fumaric acids on sourness, flavour, and overall acceptance. A combined model was also generated to measure the overall response of these sensory attributes. This study’s results demonstrated high R² values across all models, indicating strong fit and significant coefficients for main effects and interactions. Contour plots and effect plots (Piepel direction) were also obtained. The optimal acidulant mixture calculated with the combined model was 5.85 g citric acid, 4.8 g malic acid, and 4.35 g fumaric acid, yielding a combined sensory score of 100.11, showcasing the potential of these models to fine-tune sensory attributes in confectionery products. Future confirmatory experiments are recommended to validate these findings. In conclusion, this research provided a practical example of using Python to enhance product development, offering a valuable resource for researchers, students, and food developers.