Portfolio diversification is a crucial strategy for mitigating risk and enhancing long-term returns. This paper introduces a unique approach to large-scale diversification using Ant Brood Sorting clustering, a nature-inspired algorithm, in conjunction with co-integration measure of time series. Traditional diversification strategies often struggle during uncertain market times. In contrast, the proposed method leverages Ant Brood Sorting to group similar stocks based on the cointegration of their closing prices. This approach allows for the creation of diversified portfolios from a wide range of stocks. The study presents promising results, with clusters of stocks showing both high correlation and cosine similarity, validating the effectiveness of the approach. Silhouette score, a measure of cluster quality, and inter-cluster analysis demonstrate support in validating the results of the study by displaying similarities between the stocks being clustered and distinctiveness with stocks in other clusters. The research contributes to the application of nature-inspired algorithms in large-scale portfolio diversification, offering potential benefits for investors seeking resilient and balanced portfolios.