The rapid evolution of social media algorithms has created both opportunities and challenges for online consumer behavior, particularly in relation to purchasing decisions. While algorithms enhance personalization, they also raise concerns about filter bubbles, consumer autonomy, and trust in digital interactions. This study aims to investigate how algorithm-driven personalization, influencer engagement, and recommender systems collectively shape consumer purchase intentions in social commerce environments. To achieve this goal, a mixed-method research design was adopted. The first phase involved a systematic literature review and qualitative expert interviews to construct a conceptual model capturing the interplay between algorithmic personalization, trust, and social influence. The second phase employed a large-scale quantitative survey to validate the model and test hypotheses concerning the effects of algorithmic cues, influencer credibility, and trust-building mechanisms on online purchasing behavior. Findings demonstrate that algorithmic personalization significantly increases consumer engagement and purchase likelihood, but its effectiveness depends strongly on perceived credibility, transparency, and trust. Influencer marketing amplifies these effects by leveraging par asocial interaction and perceived authenticity. However, overexposure to algorithm-driven recommendations can reduce diversity in consumer choices, creating potential long-term risks for both consumers and brands. The study concludes that balancing algorithmic efficiency with transparency and ethical practices is critical for sustainable consumer trust and purchase behavior in digital markets.