Due to cybersecurity concerns, changing rules, unstable markets, and reliance on digital platforms and third parties, the quick growth of e-business has increased susceptibility to uncertainty. This study offers a systematic literature analysis that integrates the conceptualization and evaluation of e-business adaptability, elucidating the growing necessity for probabilistic risk assessment over deterministic methodologies. The review synthesizes essential adaptability dimensions-customer relationship hampered, legal concerns, poor planning, loss of reputation, and technological difficulties by analyzing studies sourced from prominent scholarly databases and evaluated against explicit inclusion/exclusion criteria. It also illustrates the interactions among these dimensions within digital ecosystems. The paper also evaluates the several probabilistic and multi-criteria methods used in related risk assessment studies, focusing on Bayesian Belief Networks (BBN), Graph Theory and Matrix Approach (GTMA), fuzzy-based models, and the Analytic Hierarchy Process (AHP). The synthesis shows that BBNs are good for figuring out what causes something to happen when you're not sure, GTMA is good for making hierarchical multi-criteria structures, but not so good for modeling uncertainty, fuzzy models are good when judgments are vague or linguistic, and AHP is a structured multi-criteria decision-making (MCDM) method. Finally, the review points out ongoing gaps, such as a lack of unified frameworks, not enough validation datasets, poor integration of real-time data, and weak hybrid probabilistic-structural models. This suggests a plan for making adaptability assessment tools that are stronger, more dynamic, and more integrated for making resilient e-business decisions.