Background: Business failure prediction has moved from single-period accounting ratio models toward longitudinal, multi-source and machine-learning approaches, yet many studies still separate financial indicators from governance, ownership and managerial information. Objective: This article develops an advanced secondary-data methodology for predicting business failure by integrating financial and non-financial indicators within a temporally disciplined predictive framework. Method: The proposed design constructs a firm-year panel from audited annual reports, stock exchange disclosures, regulatory filings and reputable commercial or open databases. Financial predictors include liquidity, leverage, profitability, cash-flow, activity and growth measures; non-financial predictors include governance, ownership, management characteristics, firm age, audit signals and industry context. The methodology combines logistic regression and discrete-time hazard models with random forests, gradient boosting and stacking, while addressing class imbalance, missingness, multicollinearity, temporal leakage and model calibration. Results: No fabricated empirical results are reported; instead, the paper specifies the analyses, validation protocol, tables and figures required for a reproducible empiricalimplementation. Contribution: The framework links established bankruptcy theory with modern predictive analytics and emphasizes out-of-time validation, probability calibration and economically meaningful error costs. The approach is designed for researchers using secondary firm-level data and can be adapted to listed-company settings in emerging markets.