The development of flexible composites is of great significance in the flexible electronic field. In combination with machine learning technology, the introduction of artificial intelligence to flexible materials design, synthesis, characterization and application research will greatly promote the flexible materials research efficiency. In this study, the back propagation (BP) neural network based on the differential evolution (DE) algorithm was applied to determine the electrical properties of the flexible Ag/poly (amic acid) (PAA) composite structure and to develop flexible materials for its different applications. In the machine learning model, the concentration of PAA, the ion exchange time of AgNO3, and the concentration and reduction time of NaBH4 are set as input parameters, and the product of the sheet resistance of the Ag/PAA film and the processing time are set as output information. To overcome the situation whereby the BP neural network solution process could fall into the local optimum, the initial threshold and the weight of the BP neural network and the data import model are optimized by the DE algorithm. Utilizing 1077 learning samples and 49 predictive samples, a machine learning model with very high accuracy was established and relative errors of predictions less than 1.96% were achieved. In terms of this model, the optimized fabrication conditions of the Ag/PAA composites, which are suitable for strain sensors and electrodes, were predicted. To identify the availability and applicability of the proposed algorithm, a strain gauge sensor, a triboelectric nanogenerator (TENG) and a capacitive pressure sensor array were fabricated successfully using the optimized process parameters. This work shows that machine learning can be used to quickly optimize the process and provide guidance for material and process design, which is of significance for the development of flexible materials and devices.