<p>Cabbage is a widely consumed vegetable in the human diet because of its low cost, broad availability and high nutritional value. The rising use of pesticides in food production creates a need to assess vegetable toxicity, which primarily results from residues in food products and environmental exposure. The study aims to offer exploration of vegetable toxicity in cabbage with the help of reliable QSTR and q-RASTR models. All the developed models were robust and predictive enough (Q²_LOO = 0.7491–0.8164, Q²F₁ = 0.5243–0.6253, Q²F₂ = 0.513–0.617, MAE_ext = 0.495–0.690). Furthermore, the reliability and predictability of models were assessed and confirmed by applicability domain and prediction reliability indicator analysis. Additionally, different machine learning models were developed to making effective predictions and multiple linear regression (MLR) comparison. Consensus approach was advocated data gap filling for USEPA ECOTOX database compounds. The most and least toxic compounds from both MLR model predictions were prioritized and analyzed. Mechanistic interpretation highlighted the structural features or fragments responsible for the agrochemical toxicity and a safe approach for designing green chemicals minimizing the toxicity. This first reported study can be useful for toxicity profiling, data gap filling and designing safer and green agrochemical for minimizing vegetable toxicity, healthy human life and environmental safety.</p> Graphical Abstract <p></p>