This paper presents a Sales Intelligence Dashboard with Predictive Modeling Capabilities designed to convert raw transactional sales data into actionable business intelligence. The system combines descriptive analytics, predictive modeling, risk scoring, and interactive visualization in a single Streamlit-based application. The implemented analytical stack includes Linear Regression for baseline estimation, Random Forest for higher-capacity prediction, Prophet-based time-series forecasting for monthly trend projection, K-Means clustering for customer segmentation, Z-score analysis for anomaly detection, churn-risk scoring, lead scoring, and RFM analysis. In addition to analytics, the platform incorporates secure login, session continuity, dataset upload history, and module-wise dashboards for business users. The proposed solution is valuable because it does not stop with charts; instead, it interprets performance, identifies risk, and suggests where decision-makers should focus attention. Compared with isolated spreadsheet-based analysis, the dashboard offers a deeper, faster, and visually clearer workflow for sales monitoring and planning.