Cervical cancer is the fourth most common cancer in women worldwide, and early detection of its precancerous lesions can decrease mortality. Cytopathology, HPV testing, and histopathology are the most commonly used tools in clinical practice. However, these methods suffer from many limitations such as subjectivity, cost, and time. Therefore, there is an unmet clinical need to develop new noninvasive methods for the early detection of cervical cancer. Here, a novel noninvasive, fast, and label-free approach with high accuracy is presented using liquid-based cytology Pap smears. CARS and SHG/TPF imaging was performed at one wavenumber on the Pap smears from patients with specimens negative for intraepithelial lesions or malignancy (NILM), and low-grade (LSIL) and high-grade (HSIL) squamous intraepithelial lesions. The normal, LSIL, and HSIL cells were selected on the basis of the ratio of the nucleus to the cytoplasm and cell morphology. Raman spectral imaging of single cells from the same smears was also performed to provide integral biochemical information of cells. Deep convolutional neural networks (DCNNs) were trained independently with CARS, SHG/TPF, and Raman images, taking into account both morphotextural and spectral information. DCNNs based on CARS, SHG/TPF, or Raman images have discriminated between normal and cancerous Pap smears with 100% accuracy. These results demonstrate that CARS/SHG/TPF microscopy has a prospective use as a label-free imaging technique for the fast screening of a large number of cells in cytopathological samples.