High‐grade serous ovarian carcinoma (HGSOC) has shown high recurrence and mortality rates despite treatment comprising cytoreductive surgery and chemotherapy. However, the recent introduction of poly(ADP‐ribose) polymerase inhibitors (PARPi) in the management of HGSOC significantly improved the prognosis. As more HGSOC patients receive PARPi treatment, accurately predicting the treatment response becomes crucial. In this study, we first establish the proteomic landscape of ovarian cancer according to PARPi response. Protein signatures were validated in the independent cohort, and preliminarily investigated their potential roles in PARPi resistance. To identify protein signatures associated with PARPi resistance, we conducted an in‐depth quantitative proteomic analysis of formalin‐fixed paraffin‐embedded (FFPE) cancer tissues (n = 24) from platinum‐sensitive recurrent HGSOC patients using a tandem mass tag (TMT) 10‐plex quantification strategy. According to PARPi treatment response, a total of 187 differentially expressed proteins (DEPs) were identified (p < 0.05, |fold‐change| > 1.2), with 54 and 133 exhibiting higher expression in the good and poor response groups, respectively. Next, we conducted feature selection with leave‐one‐out cross‐validation to identify protein signatures that stratify the response to PARPi. Machine learning algorithms yielded lists of top proteins selected by each respective algorithm. Among these, we identified three common proteins: stearoyl‐CoA desaturase 5 (SCD5), NUDT4 and GRP107 which were upregulated in the poor response group. For validation, we performed data‐independent acquisition‐based proteomics using an independent set of FFPE tissues (three good and seven poor responders). The SCD5 showed high predictive performance in identifying poor responders (area under the receiver operating characteristic curve, 0.952). From a clinical viewpoint, for HGSOC patients who are identified as at high risk of showing poor response to PARPi maintenance therapy via pretreatment proteomics analyses, physicians may deter the use of PARPi, maximize the effect of PARPi combined with other therapy, or recommend intensive surveillance during PARPi maintenance to detect recurrence earlier. Our findings broaden biological insights into predicting PARPi responses