Open-pit mine production scheduling plays a critical role in maximizing economic returns while meeting environmental and operational constraints. However, the widespread use of proprietary software and rigid deterministic frameworks often limits accessibility, scalability, and adaptability; particularly for small and medium scale operations. This study presents a scalable and sustainable production scheduling model developed entirely in Python, using open-source libraries to bridge this gap. The model employs linear integer programming to optimize the extraction sequence of ore blocks, maximizing Net Present Value (NPV) while respecting precedence relationships, production capacity limits, and environmental penalties embedded in block valuations. Using synthetic block model data, the framework achieved simulated returns exceeding $1.6 billion USD, with integrated visualization tools including 3D diagrams, histograms, and boxplots; enhancing result interpretation. The model's modular and transparent design allows easy adaptation to different mine contexts and planning scenarios. By eliminating reliance on costly platforms and embedding sustainability into the optimization process, this work contributes a practical, technically rigorous, and accessible alternative for modern mine planning. It supports the broader transition toward intelligent, responsible, and inclusive scheduling systems aligned with environmental, social, and governance (ESG) expectations.