We propose a novel cl ass of change of measure inequalities via a unified framework based on the data processing inequality for f-divergences\, which is surprisingly elem entary yet powerful enough to yield tighter inequalities. We provide chang e of measure inequalities in terms of a broad family of information measur es\, including f-divergences (with Kullback-Leibler divergence and $\\chi^ 2$-divergence as special cases)\, Renyi divergence\, and $\\alpha$-mutual information (with maximal leakage as a special case). A key advantage of o ur framework is its flexibility: it readily adapts to a range of settings\ , including the generalization error analyses\, the conditional mutual inf ormation framework\, PAC-Bayesian theory\, differential privacy mechanisms and data memorization problem\, with simplified analyses