Monitoring solution parameters is of utmost importance in various industries and daily applications. However, the challenge lies in using a single sensor to effectively monitor different parameters in the solution. In this study, a dual-mode sensor is proposed, capable of monitoring multiple solution parameters combined with deep learning method. The fabrication process of the dual-mode sensor is simple, involving a substrate, interdigital electrodes, and a dielectric layer. The sensitivity of the dual-mode sensor is improved by increasing the dielectric constant of the dielectric layer and optimizing the design of the interdigital electrodes. Under the capacitive sensing mode, the sensor effectively identifies solution type by detecting capacitance changes due to the conductivity of the mixed solution. Under the triboelectric sensing mode, the sensor exhibits high sensitivity to solution concentration through the coupling of the capacitive enhancement effect and the triboelectric effect. An electric switch is incorporated into the design to control the signal acquisition of the dual-mode sensor. By combining the deep learning method with the dual-mode sensor, high recognition accuracies have been achieved for both solution type and concentration, with average accuracies exceeding 95%. Furthermore, the dual-mode sensor is not limited to monitoring liquid droplets; it can also be used for monitoring the types of liquids in bottles. In addition, an intelligent system is developed to visualize the intelligent monitoring process. This work not only contributes to a better understanding of the underlying mechanisms of planar capacitive sensors (PCS) and free-standing triboelectric nanogenerators (FS-TENG), but also presents a promising method for intelligent solution monitoring.