Similarity search in high-dimensional spectral datasets is critical for applications in analytical chemistry, bioinformatics, and material science. Conventional methods often struggle with variability in peak positions, intensities, and noise, limiting their effectiveness for large-scale spectral comparison. In this paper, we propose a peak pattern based similarity search framework that abstracts spectra into robust peak representations and performs flexible, metric-based comparisons. The approach integrates preprocessing techniques such as noise filtering, normalization, and peak detection, followed by peak alignment with tunable tolerance N and matching threshold \(\delta \) . Similarity is quantified using intensity-weighted metrics designed to accommodate spectral distortions and scaling variations. Experimental validation on real-world high-dimensional spectral datasets demonstrates that the framework achieves efficient and accurate retrieval of similar spectra. Parameter analysis highlights the impact of alignment tolerance and intensity weighting on retrieval performance, showing improved robustness against noise and peak shifts.