Bayes' rule has enabled innumerable powerful algorithms of statistical signal processing and statistical machine learning. However, when model misspecifications exist in prior and/or data distributions, the direct application of Bayes' rule is questionable. Philosophically, the key is to balance the relative importance between the prior information and the data evidence when calculating posterior distributions: If prior distributions are overly conservative (i.e., exceedingly spread), we upweight the prior belief; if prior distributions are overly aggressive (i.e., exceedingly concentrated), we downweight the prior belief. The same operation also applies to likelihood distributions, which are defined as normalized likelihoods if the normalization exists. This paper studies a generalized Bayes' rule, called uncertainty-aware (UA) Bayes' rule, to technically realize the above philosophy, thus combating model uncertainties in prior and/or data distributions. In particular, the advantage of the proposed UA Bayes' rule over the existing power posterior (i.e., $α$-posterior) is investigated. Applications of the UA Bayes' rule on classification and estimation are discussed: Specifically, the UA naive Bayes classifier, the UA Kalman filter, the UA particle filter, and the UA interactive-multiple-model filter are suggested and experimentally validated.