Aging clocks have become a significant breakthrough in aging biology, offering potential indicators for aging interventions and age-related disease prevention. This study demonstrates that accumulating stochastic variation in simulated data is sufficient to build aging clocks, and that first- and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data. The results suggest that stochastic accumulation in any data set with a ground state at age zero is sufficient for generating aging clocks. The study shows that datasets with accumulating stochastic variation, normalized between 0 and 1, can be used to build age predictors, indicating that any biological measurement could be used to build accurate aging clocks. The pace of predicted aging is primarily determined by the degree of stochastic variation, with increased variation accelerating and reduced variation decelerating the predicted age. Predictions of a transcriptomic aging clock for Caenorhabditis elegans significantly correlate with the amount of added stochastic variation. The predictive results of a clock based on simulated transcriptomic data with accumulating stochastic variation significantly correlate with chronological age. Epigenetic aging clocks measure the amount of stochastic variation accumulated, and the predictive results of a model trained on simulated data with accumulating stochastic variation significantly correlate with the chronological age of human DNA methylation samples. The study validates and replicates results on data from the Mammalian Methylation Consortium, showing that various mammalian species and interventions can be correctly predicted. The accumulation of stochastic variation enables the construction of pan-mammalian clocks, capable of detecting biological age deceleration and acceleration, and the rejuvenation trajectory over a reprogramming time-course in human cells. The analyses suggest that aging clocks could be based on any biological parameter with stochastic age-related alterations for precise aging measurements without the need for a deterministic process. The study shows that datasets with accumulating stochastic variation can be used to build age predictors, and that any biological measurement could be used to build accurate aging clocks. The pace of predicted aging is primarily determined by the degree of stochastic variation, with increased variation accelerating and reduced variation decelerating the predicted age. Predictions of a transcriptomic aging clock for Caenorhabditis elegans significantly correlate with the amount of added stochastic variation. The predictive results of a clock based on simulated transcriptomic data with accumulating stochastic variation significantly correlate with chronological age. Epigenetic aging clocks measure the amount of stochastic variation accumulated, and the predictive results of a model trained on simulated data with accumulating stochastic variation significantly correlate with the chronological age of human DNA methylation samples.Aging clocks have become a significant breakthrough in aging biology, offering potential indicators for aging interventions and age-related disease prevention. This study demonstrates that accumulating stochastic variation in simulated data is sufficient to build aging clocks, and that first- and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data. The results suggest that stochastic accumulation in any data set with a ground state at age zero is sufficient for generating aging clocks. The study shows that datasets with accumulating stochastic variation, normalized between 0 and 1, can be used to build age predictors, indicating that any biological measurement could be used to build accurate aging clocks. The pace of predicted aging is primarily determined by the degree of stochastic variation, with increased variation accelerating and reduced variation decelerating the predicted age. Predictions of a transcriptomic aging clock for Caenorhabditis elegans significantly correlate with the amount of added stochastic variation. The predictive results of a clock based on simulated transcriptomic data with accumulating stochastic variation significantly correlate with chronological age. Epigenetic aging clocks measure the amount of stochastic variation accumulated, and the predictive results of a model trained on simulated data with accumulating stochastic variation significantly correlate with the chronological age of human DNA methylation samples. The study validates and replicates results on data from the Mammalian Methylation Consortium, showing that various mammalian species and interventions can be correctly predicted. The accumulation of stochastic variation enables the construction of pan-mammalian clocks, capable of detecting biological age deceleration and acceleration, and the rejuvenation trajectory over a reprogramming time-course in human cells. The analyses suggest that aging clocks could be based on any biological parameter with stochastic age-related alterations for precise aging measurements without the need for a deterministic process. The study shows that datasets with accumulating stochastic variation can be used to build age predictors, and that any biological measurement could be used to build accurate aging clocks. The pace of predicted aging is primarily determined by the degree of stochastic variation, with increased variation accelerating and reduced variation decelerating the predicted age. Predictions of a transcriptomic aging clock for Caenorhabditis elegans significantly correlate with the amount of added stochastic variation. The predictive results of a clock based on simulated transcriptomic data with accumulating stochastic variation significantly correlate with chronological age. Epigenetic aging clocks measure the amount of stochastic variation accumulated, and the predictive results of a model trained on simulated data with accumulating stochastic variation significantly correlate with the chronological age of human DNA methylation samples.