Background: The estrogen alpha receptor (ERα) is critical in breast carcinogenesis. Although selective estrogen receptor modulators like tamoxifen are clinically used, their adverse effects highlight the need for safer alternatives. The study uses computational methods to identify potential ERα inhibitors within a benzimidazole scaffold. Methodology: This study employed computational approaches, including pharmacophore generation, 3D-QSAR, virtual screening, molecular docking, and in silico ADME/Tox analysis. The best pharmacophore model (DDRRR_1) identified two hydrogen donors and three aromatic rings as critical features. Moreover, a rigorous external validation was used on decoy databases with optimized metrics (ROC, BEDROC, AUROC). A subsequent atom-based 3D-QSAR model with a high correlation coefficient (R² = 0.9), cross-validated coefficient (Q² = 0.8), and Fisher ratio (F = 80.1) was developed. Benzimidazole scaffolds from PubChem were screened, followed by docking against ERα (PDB ID: 3ERT) and ADMET profiling. Results and Discussion: The pharmacophore model validated the importance of the identified features. The 3D-QSAR model effectively screened benzimidazole scaffolds, with five component PLS factors, supporting the pharmacophore findings. This model effectively screened benzimidazole scaffolds obtained from the PubChem database, followed by molecular docking against the targeted protein ERα (PDB ID: 3ERT) and identified five promising compounds. ADME/Tox profiling revealed PubChem ID 3074802 (2-[2-(1H-indol-3-yl) ethyl]1H-benzimidazole) has favourable pharmacokinetics and a low toxicity profile. Conclusion: These findings indicate that PubChem ID 3074802 is a promising candidate for further therapeutic drug development in breast cancer treatment. It demonstrates the highest binding affinity (-9.842 kcal/mol) compared to the standard drug Tamoxifen (-5.357 kcal/mol) and exhibits a favorable ADME/Tox profile.