Wearable electronics have garnered much attention owing to benefits such as closer integration of form with function, flexibility, and light-weighting. Power sources used with FHE may be subject to dynamic flexing in addition to flex-to-install during the usage life of the product. While thick block batteries have been studied extensively in prior research — the impact of stresses of daily motion on the state of health degradation of thin-flexible batteries in conjunction with the use parameters is not well understood. Use conditions, including storage duration, operating temperature, flexing frequency, interval, and flex radius, might vary. Machine learning (ML) methods are needed prediction of state-of-health (SOH) degradation of the battery in various environmental conditions. It is not cost-effective to measure battery response in every condition, while the ANN ML might be able to assess conditions not previously measured. In this regard, it is expected that the ANN might be able to train the simulation. In this study, the simulation of SOH degradation on charging/discharging the flexible battery in dynamic folding, twisting and static folding with a calendar-aged battery in high temperature have been conducted. Accordingly, the ANN ML model has been trained with the simulation datasets to substitute the simulation. The generated data will be used for cross-validation of ML model and simulation for the battery life prediction. There is an expectation that such a combined method for data analysis might be helpful for time efficiency and cost reduction of research.