This study presents the development and implementation of a Markov Chain-based model to analyze and forecast daily rainfall patterns in Bogor City, Indonesia, a region known for its high precipitation. Using daily rainfall data from July 22, 2024, to July 21, 2025, the rainfall intensities were classified into five categories according to BMKG standards. A transition probability matrix was constructed by calculating the relative frequencies of state changes, and multi-step forecasts were performed using the Chapman-Kolmogorov equation. The model reached a steady state after 16 iterations, with results showing an accuracy of 60%, a recall of 100%, and a precision of 55.56%. These findings indicate that the model performs well in identifying rainfall events, especially light rain, but also produces a notable number of false positives. To demonstrate practical application, a mobile weather prediction tool was developed using the Flutter framework, enabling users to input data and visualize forecast results interactively. The application offers a lightweight, cross-platform solution for localized weather prediction. While the current model is effective for early warning purposes, future improvements are necessary to enhance prediction precision. Overall, this research integrates mathematical modeling with mobile technology to support informed decision-making in high-rainfall regions like Bogor.