The consistent decrease in the open ratio of wafers has spurred a demand for advanced endpoint detection (EPD) techniques to ensure accurate plasma etching in nonlinear optical emission spectroscopy (OES) data characterized by a low signal-to-noise ratio. Additionally, precise detection of endpoint is hindered by variations between plasma chambers arising from diverse issues. To address these issues, this study proposes a nonlinear manifold learning-based EPD model and a chamber condition identification framework. The EPD model demonstrates the capability to extract endpoint-related latent variables from complex nonlinear OES data. Moreover, the model exhibits the ability to generalize to larger datasets through density-based time series clustering. The chamber condition identification framework not only classifies plasma conditions but also automates the determination of the conditions for incoming new wafers. Evaluation of the proposed approach, conducted using actual OES data from multiple chambers, demonstrated that the EPD model outperformed other models which are based on diverse dimensionality reduction approaches. Furthermore, the chamber condition identification process successfully identified condition variations and accurately determined the plasma condition of new data. Moreover, conducting EPD modeling for separate conditions rather than collectively for diverse conditions demonstrated superior detection results, underscoring the importance of the chamber condition identification process.