This research investigates the potential of machine learning (ML) for constructing portfolios that outperform human-based management, specifically focusing on the Indian midcap market. The study compares AI-based portfolio compositions, optimised using various risk measures, to the holdings of top midcap mutual funds. In this research, the top five midcap mutual funds’ sectoral distributions, portfolio compositions, and AI-generated portfolios are examined. According to the research, there is significant performance potential in the AI-generated portfolio, particularly when taking shorter investment horizons into account. Portfolios that maximise the Sharpe ratio produced the best returns throughout the course of the test period for four out of the six sectors, according to the research statistics. Additionally, in order to shed light on the effectiveness and possible advantages of our strategy, our study compares the suggested technique to existing investing strategies that concentrate on particular corporations as well as well-established market benchmarks. The research shows that, particularly when taking shorter investment horizons into account, the AI-generated portfolio has great performance potential.