Artificial intelligence (AI) techniques are currently gaining momentum in every corner of the study and analysis, including optical fiber sensing. These techniques are more optimal and robust for designing unprecedented levels of precision and functionality in optical fiber sensing system. This chapter provides a clear picture of different steps and operations in designing AI-powered optical sensor environment. The varying sensor data from different sensor type has different characteristics. This chapter clearly defines the characteristics of different types sensor datasets and suitable pre-processing steps for converting the dataset suitable for AI model learning. Moreover, important feature extraction methods are also discussed which are different for each optical sensor datasets and essential in determining patterns and relationships in sensor data. In subsequent part of the chapter AI model selection methodology, evaluation metrics selection for different analysis and data types with data augmentation methods and hyperparameter optimization methods are clearly devised for optimal decision-making in the field of optical fiber sensors. This multi-dimensional analysis can act as a guideline for future researchers and practitioners to navigate the complexities and harness the full potential of AI-powered optical fiber sensing.