The goal of the project is to automate the conversion of pseudocode into visually appealing flowcharts, facilitating the transition from human-readable algorithm descriptions to machine-executable code in software. This study presents a new approach that uses the OpenAI GPT-3.5 Turbo model for automatic flowchart generation. Addressing the lack of tools that understand pseudocode, the method interprets pseudocode, generates executable code fragments, and creates aesthetically pleasing flowcharts. This article reviews the literature and highlights a gap in automated flow charts. The selected methodology uses state-of-theart language models, with a special focus on natural language processing (NLP). The study aims to improve the understanding of algorithms by visually representing complex algorithms through automatic flowchart generation.Examples 1. Sorting rhythm: Consider a scenario where a complex sorting algorithm is described in pseudocode. The flowchart generator interprets the pseudocode, generates the corresponding executable code, and converts it into a complete flowchart. This visual representation helps developers understand algorithm logic, encourages discussion, and improves collaboration.2. Recursive algorithms and tree structures: The project extends the application to handle recursive algorithms and tree structures. For example, when a flowchart generator is provided with recursive pseudocode to traverse a binary tree, it smoothly transforms the abstract description into a detailed flowchart. This feature increases the versatility of the toolbox and offers a wide range of algorithmic models.3. Optimization Algorithm Performance: The flowchart generator not only helps to understand but also to improve algorithms by identifying potential bottlenecks and optimizing the generated code. Critical decision points and loops in the flowchart visually highlight key areas and provide developers with valuable information to make informed optimization decisions.