The article presents a systematic review of theoretical approaches to cash flow modeling for assessing the financial sustainability of organizations. The study is based on an interdisciplinary analysis encompassing economic-mathematical modeling, machine learning algorithms, structural equation modeling methods, and stochastic programming. Special attention is given to the classification of models by data type, level of predictive and explanatory power, and their applicability under conditions of high uncertainty and digital transformation. The advantages and limitations of classical liquidity management models are examined, along with the potential of neural network and ensemble methods in analyzing complex nonlinear relationships, and the resilience of stochastic strategies under stress scenarios. Key non-financial constructs—such as information systems, leadership, and social capital—are identified as having a significant impact on the structure and dynamics of cash flows and contributing to institutional resilience. The integration of predictive and structural factors into hybrid architectures for resilience assessment is analyzed, providing a balance between model accuracy and interpretability. The article offers an original review of models aimed at strategic financial planning in the context of digitalization and organizational transformation. This study will be of interest to researchers in corporate finance, strategic management, applied economics, and practitioners developing resilience models and adaptive cash flow management systems in volatile environments.
Gulab Singh Yadav (Wed,) studied this question.