Atopic dermatitis (AD), psoriasis and actinic keratosis (AK) are common skin diseases that present as inflamed, red scaly patches. They often have overlapping clinical features, making their diagnosis and classification difficult. The objective of the current study is to establish proteomic signatures for reliable, non-invasive diagnosis and classification of AD, psoriasis, and AK while providing insights into the underlying molecular pathways and biological functions of these conditions. A total of 67 scarless skin samples were collected from patients with AD (n=20), psoriasis (n=10), AK (n=20), and normal skin (n=17) using adhesive discs. These samples were analysed using data-independent acquisition mass spectrometry-based proteomics (DIA-MS) to identify and quantify proteins. Differential abundance analysis and bioinformatic analysis were then performed on the proteomic data. A total of 2202 protein groups were identified from the samples studied, pinpointing proteins such as IGHG4, IL36a, and PSMD14 that could differentiate between AD, psoriasis, or AK compared to other lesion groups, respectively. Principal component analysis correctly classified a significant proportion of the samples based on their clinical diagnosis, while support vector machine analysis correctly classified 76.5% of AD, 77.8% of psoriasis and 78.9% of AK samples according to their clinical diagnosis. Pathway Ingenuity Pathway Analysis (IPA) of the data revealed significant dysregulation of key pathways related to inflammation, immune responses, and endocytosis in different lesions. This study successfully identified lesion-specific biomarkers and biological functions through proteomic analysis of scarless skin samples from AD, psoriasis, and AK patients. These findings offer promise for the development of a non-invasive diagnostic and classification method for these skin conditions.