By combining picture and video feature extraction, the sentiment and sarcasm analysis method aims to transform social media analytics. Conventional sentiment analysis mostly concentrates on text, frequently ignoring the important contextual and emotional information found in visual and aural material. In sarcasm detection, which is essential for precisely comprehending consumer pleasure, brand impression, and product awareness, this difference is particularly troublesome. Deep Learning techniques are used in the suggested approach to address these constraints and provide a thorough multimodal analysis. Our technology combines contextual analysis from photos and videos, facial expressions and body language to capture the underlying emotions of user attitudes that text-only systems frequently overlook. This multimodal approach gives a more accurate and perceptive sentiment analysis that captures the genuine emotional undertones.