In this study, an adaptive continuous wavelet transform (CWT)-based overcurrent protection for smart grids is proposed to enhance the overcurrent protection performance encountering high-impedance fault (HIF) and current transformer (CT) saturation, which are extremely complex phenomena and their impacts often cause mis-coordination or mal-operation. The proposed algorithm samples three phase current waveforms and imports them to CWT to extract high frequency coefficients. Afterwards, the sum of absolute values of the coefficients, S-coef, is calculated for each sample during the last cycle. Meanwhile, several simulations related to HIFs with different impedances and CT saturations with different severities are executed and the fault currents and the sum of absolute values of the coefficients are achieved and saved in the relay memory as (X, Y) coordinates of points (IL-fault, SL-coef), named learning data'. Thereafter, each S-coef is imported to the X-Y plane and, consequently, the occurrence of HIF or CT saturation is detected and the real-fault current is estimated by both non-linear interpolation and extreme learning machine approaches. Subsequently, new time dial setting and I-pickup of the relays are computed and reloaded. Security, dependability, and sensitivity of the proposed adaptive protection method are confirmed by numerous simulation studies.