The rapid proliferation of social media platforms has exposed users to a variety of cyber threats, necessitating advanced measures for detection and mitigation. This study aims to 1. identify the predominant cyber threats targeting social media platforms, 2. explore the role of digital forensics technologies enhanced by artificial intelligence (AI) and machine learning (ML) in mitigating these threats, and 3. examine the challenges in establishing standardized frameworks for detecting social media attacks. Employing a descriptive-analytical approach, the study collected data from 151 cybersecurity experts, IT professionals, and social media managers using a structured questionnaire. The questionnaire measured familiarity with cyber threats, the effectiveness of AI/ML in digital forensics, and obstacles in creating unified frameworks. Reliability was confirmed through a pilot study, with Cronbach's Alpha coefficients ranging from 0.72 to 0.81. Findings indicate high awareness of common threats such as phishing and ransomware (mean = 3.74), reflecting the success of public awareness campaigns and professional training. However, moderate familiarity with malware (mean = 3.51) and advanced technological tools (mean = 3.57) reveals significant knowledge gaps. Challenges in developing a unified cybersecurity framework were rated highly (mean = 3.78), with key issues including standardizing protocols, regulatory barriers, and limited stakeholder collaboration. While demographic factors like gender, age, and educational background had no significant influence on perceptions, professional experience emerged as a critical factor, with seasoned professionals demonstrating greater familiarity with digital forensics and AI/ML technologies.