Algorithmic pricing is transforming competition by enabling firms to monitor rivals, adjust prices instantly, and sustain collusive outcomes without direct human coordination. Traditional antitrust laws, built around proof of agreement and communication, struggle to address autonomous systems that learn collusion through machine learning. This article analyses global and Indian approaches to algorithmic collusion and proposes a foreseeability-based framework for liability and enforcement.