Pesticides directly pollute the environment and contaminate foods, ultimately being absorbed by the human body. Its residues are highly toxic, which have serious effects on human health. Some methods such as chromatography and gas/liquid chromatography-mass spectrometry have been widely used to detect pesticide residues. However, these methods also have some disadvantages, such as complicated pre-processing steps, time-consuming and labor-intensive. Surface-enhanced Raman spectroscopy (SERS) technology is regarded as a new pesticide residue detection method due to its high sensitivity, good specificity, comprehensive fingerprint information and no damage to the sample. It can realize trace pesticides in liquid or solid samples through simple extraction. In this review, to provide new references in the detection of pesticide residues, we mainly summarized the research progress of SERS detection technology and methods for pesticide residues from the three aspects of the preparation of SERS active substrates, detection methods, and intelligent analysis of spectra. In preparing SERS active substrates, single noble metal sol nanoparticles have poor stability and sensitivity due to random and uncontrollable “hot spots”, which can no longer satisfy trace pesticide residue detection. In order to improve the adsorption capacity of the SERS substrate more target analytes are enriched on the surface of the SERS substrate and the signal does not change significantly. The single noble metal sol nanoparticles are assembled, or its surface is modified by adding chemicals, inert materials, etc., to prepare uniform SERS composite substrate, thereby effectively and specifically capturing the analyte, ensuring good reproducibility and sensitivity of SERS signal. On this basis, in order to achieve the specificity and high sensitivity detection, the detection method of SERS for pesticide residues has gradually evolved from the use of simple nanoparticles such as gold and silver nanoparticles as an enhanced substrate to the optimization of sample pretreatment techniques, the preparation of specific SERS probes by chemical modification, breakthroughs in the physical structure of enhanced substrates, and dynamic SERS(D-SERS) detection. After obtaining the Raman spectrum of the substance, the effective Raman characteristic region is usually within a short wavenumber range, and the spectral data is as high as thousands of dimensions. There is more redundancy, which leads to an increase in the complexity of subsequent analysis. SERS spectrum intelligence analysis often uses chemometrics methods to pre-process the original spectrum, extract features and modeling, realize data dimensionality reduction and main information extraction, and then achieve qualitative and quantitative for pesticide residues. In order to obtain global features and large-scale process data, deep learning methods have also been introduced into SERS spectral intelligent analysis in recent years, which has achieved good analysis results. In summary, SERS has an excellent development prospect for rapid detection of pesticide residues and can provide new ideas for future analysis and testing field.