This study addresses the conflict between data utilization and consumer data protection in firms’ AI-driven pricing. A game-theoretic framework incorporating consumer data manipulation behavior is proposed. The monopolist’s optimal pricing strategy and consumers’ optimal data manipulation efforts, as well as the impact of data manipulation, are investigated. The results show that when the algorithm development cost is relatively low, the AI-driven pricing strategy yields the highest profit. When the algorithm development cost is relatively high and the proportion of high-value consumers is high, a uniform high price is optimal. When the algorithm development cost is relatively high and the proportion of high-value consumers is low, a uniform low price is optimal. Consumer data manipulation significantly reduces the decision space of AI-driven pricing, reduced the firm’s willingness to invest in algorithms, and increases consumer surplus, whereas the cost of consumer data manipulation has an expanding effect on the strategic space of AI pricing.