Emotion detection from facial expressions is a rapidly advancing field within computer vision and artificial intelligence, with profound implications for human-computer interaction, mental health assessment, marketing research, and driver safety systems. This paper provides a comprehensive overview of developing systems for recognizing human emotions such as happiness, sadness, anger, surprise, fear, disgust, and neutrality from facial images or video streams using Python, artificial intelligence (AI), and machine learning (ML) techniques. It covers the entire pipeline, including data acquisition and preparation, facial detection, preprocessing, feature extraction methodologies both handcrafted and learned, selection and training of appropriate ML/AI models, and robust evaluation strategies. The paper also highlights key Python libraries and frameworks essential for implementation, discusses common challenges such as variations in expression intensity and cultural differences, and outlines potential future study directions in this dynamic area.